{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Preprocessing of Mutation Frequencies\n",
    "This notebook is devoted to the preprocessing of TCGA mutation frequencies. I will derive a matrix, containing the mutation frequencies per samples (columns) and genes (rows).\n",
    "This matrix will later be the feature matrix used by another notebook to form a hdf5 container."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy.matlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import mygene\n",
    "import h5py\n",
    "import os, sys, math\n",
    "import umap\n",
    "from functools import reduce\n",
    "\n",
    "sys.path.append(os.path.abspath('../preprocessing/'))\n",
    "import preprocessing_utils as utils\n",
    "\n",
    "bestSplit = lambda x: (round(math.sqrt(x)), math.ceil(x / round(math.sqrt(x))))\n",
    "plt.rc('font', family='Times New Roman')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Parameters\n",
    "* `ULTRA_MUTATED_SAMPLES_THRESHOLD`: This value tells us how many mutations (CNAs + SNVs) a sample is allowed to have before we remove it. The reason behind this filtering step is that there are some samples with accumulated mutations that do not neccessarily contribute\n",
    "* `USE_CNAS`: Use CNA information or only SNVs\n",
    "* `NORMALIZE_FOR_GENE_LENGTH`: Whether dividing SNV frequencies by the length of the gene or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ULTRA_MUTATED_SAMPLES_THRESHOLD = 1000\n",
    "USE_CNAS = False\n",
    "NORMALIZE_FOR_GENE_LENGTH = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "gencode_annotation_path = '../../data/pancancer/gencode/gencode.v28.basic.annotation.gff3'\n",
    "snv_base_dir = '../../data/pancancer/TCGA/mutation/download_new/'\n",
    "cna_base_dir = '../../data/pancancer/TCGA/mutation/CNA/'\n",
    "ultra_mutated_samples_path = '../../data/pancancer/TCGA/mutation/ultramutated_tumor_ids'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Preprocessing SNVs\n",
    "I downloaded one MAF file from TCGA. Those files have to be pre-processed, roughly following the data processing from HotNet2.\n",
    "I decided to do the following steps:\n",
    "1. Loading the MAF as `DataFrame`\n",
    "2. Removing non-silent mutations (_step 2.ii in calling from HotNet2 preprocessing_)\n",
    "3. Removing ultra-mutators (_step 1.i in filtering from HotNet2 preprocessing._)\n",
    "4. Compute $Gene \\times Sample$ matrix $S_c$ for each cancer type $c$\n",
    "5. Normalize for the gene length using the [GENCODE basic annotation](ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/gencode.v29.basic.annotation.gff3.gz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "trim_fun = lambda x: '-'.join(str(x).split('-')[:4]) # TCGA barcode including sample\n",
    "\n",
    "def get_gene_sample_matrix(path, ultra_mutated_samples_path, gene_lengths=None):\n",
    "    maf = pd.read_csv(path, compression='gzip', sep='\\t', comment='#', header=0)\n",
    "    non_silent = maf[maf.Variant_Classification != 'Silent'] # remove silent mutations\n",
    "    #non_silent = maf\n",
    "    print (\"Removed {} (of {}) mutations because they are silent\".format(maf.shape[0]-non_silent.shape[0],\n",
    "                                                                         maf.shape[0]))\n",
    "    non_silent.Tumor_Sample_Barcode = non_silent.Tumor_Sample_Barcode.map(trim_fun)\n",
    "\n",
    "    # remove ultra-mutators\n",
    "    ultra_mutated_samples = pd.read_csv(ultra_mutated_samples_path, header=None, names=['Tumor_IDs'])\n",
    "    ultra_mutated_samples.Tumor_IDs = ultra_mutated_samples.Tumor_IDs.map(trim_fun)\n",
    "    maf_no_ultra = non_silent[~non_silent.Tumor_Sample_Barcode.isin(ultra_mutated_samples.Tumor_IDs)]\n",
    "    print (\"Left with {} SNVs after removing {} mutations in hyper-mutated samples\".format(maf_no_ultra.shape[0], non_silent.shape[0]-maf_no_ultra.shape[0]))\n",
    "    \n",
    "    # compute gene x sample matrix\n",
    "    gene_barcode_mat = maf_no_ultra.groupby(['Hugo_Symbol', 'Tumor_Sample_Barcode']).size().reset_index().rename(columns={0:'count'})\n",
    "    assert ((gene_barcode_mat.pivot(index='Hugo_Symbol',\n",
    "                                    columns='Tumor_Sample_Barcode',\n",
    "                                    values='count'\n",
    "                                   ).sum() == maf_no_ultra.groupby('Tumor_Sample_Barcode').count().Hugo_Symbol).all())\n",
    "    assert ((gene_barcode_mat.pivot(index='Hugo_Symbol',\n",
    "                                    columns='Tumor_Sample_Barcode',\n",
    "                                    values='count'\n",
    "                                   ).sum(axis=1) == maf_no_ultra.groupby('Hugo_Symbol').count().Tumor_Sample_Barcode).all())\n",
    "    gene_sample_matrix = gene_barcode_mat.pivot(index='Hugo_Symbol', columns='Tumor_Sample_Barcode', values='count').replace(np.NaN, 0)\n",
    "    \n",
    "    # lastly, normalize for gene length if requested\n",
    "    if not gene_lengths is None:\n",
    "        bpmr = gene_sample_matrix.sum() / float(gene_lengths.sum()) # mutation rate per base per patient (vector)\n",
    "        expected_mutation_rates = pd.DataFrame(np.matlib.repmat(bpmr, gene_lengths.shape[0], 1) * gene_lengths.values.reshape(-1, 1),\n",
    "                                               index=gene_lengths.index,\n",
    "                                               columns=bpmr.index\n",
    "                                              ) # expected mutation frequency per gene and patient\n",
    "        # normalized mutation frequency (actual mutations divided by expected mutations)\n",
    "        # We add 1 to the dividend to avoid division by 0 or too much influence of short genes\n",
    "        denom = 1 + expected_mutation_rates.reindex_like(gene_sample_matrix).fillna(expected_mutation_rates.median(axis=0))\n",
    "        normalized_gene_sample_matrix = gene_sample_matrix / denom\n",
    "        return normalized_gene_sample_matrix\n",
    "    else:\n",
    "        return gene_sample_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '20 Longest Proteins')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "annotation_gencode = pd.read_csv(gencode_annotation_path,\n",
    "                                 comment='#', sep='\\t', skiprows=7,header=None,\n",
    "                                 names=['chr', 'source', 'type', 'start', 'end', 'score', 'strand', 'phase', 'attr']\n",
    "                                )\n",
    "# derive length of all exons\n",
    "annotation_gencode = annotation_gencode[annotation_gencode.type == 'exon']\n",
    "annotation_gencode['length'] = np.abs(annotation_gencode.end - annotation_gencode.start)\n",
    "# extract the gene name for each exon\n",
    "def get_hugo_symbol(row):\n",
    "    s = row.attr\n",
    "    for elem in s.split(';'):\n",
    "        if elem.startswith('gene_name'):\n",
    "            return elem.split('=')[1].strip()\n",
    "    return None\n",
    "annotation_gencode['Hugo_Symbol'] = annotation_gencode.apply(get_hugo_symbol, axis=1)\n",
    "# add length of exons together for each gene\n",
    "exonic_gene_lengths = annotation_gencode.groupby('Hugo_Symbol').length.sum()\n",
    "\n",
    "# plot the longest proteins at the end\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "plt.barh(y = range(20), width=exonic_gene_lengths.sort_values(ascending=False).head(20), tick_label=exonic_gene_lengths.sort_values(ascending=False).head(20).index)\n",
    "plt.xlabel('Exonic Length in Base Pairs')\n",
    "plt.title('20 Longest Proteins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (87,98,118) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 11029 (of 64804) mutations because they are silent\n",
      "Left with 50113 SNVs after removing 3662 mutations in hyper-mutated samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/pandas/core/generic.py:5303: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed READ with 135 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (87,88,98,118) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 44644 (of 213144) mutations because they are silent\n",
      "Left with 168500 SNVs after removing 0 mutations in hyper-mutated samples\n",
      "Processed STAD with 437 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (87,88,118) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 41350 (of 208180) mutations because they are silent\n",
      "Left with 164350 SNVs after removing 2480 mutations in hyper-mutated samples\n",
      "Processed LUAD with 563 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (19,20,28,87,88) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 18759 (of 103405) mutations because they are silent\n",
      "Left with 84646 SNVs after removing 0 mutations in hyper-mutated samples\n",
      "Processed CESC with 289 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (87) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 7505 (of 45313) mutations because they are silent\n",
      "Left with 37808 SNVs after removing 0 mutations in hyper-mutated samples\n",
      "Processed ESCA with 184 samples\n",
      "Removed 150337 (of 886377) mutations because they are silent\n",
      "Left with 595030 SNVs after removing 141010 mutations in hyper-mutated samples\n",
      "Processed UCEC with 512 samples\n",
      "Removed 22391 (of 120988) mutations because they are silent\n",
      "Left with 95408 SNVs after removing 3189 mutations in hyper-mutated samples\n",
      "Processed BRCA with 977 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (88,118) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 10010 (of 54238) mutations because they are silent\n",
      "Left with 44228 SNVs after removing 0 mutations in hyper-mutated samples\n",
      "Processed LIHC with 364 samples\n",
      "Removed 4192 (of 23765) mutations because they are silent\n",
      "Left with 19573 SNVs after removing 0 mutations in hyper-mutated samples\n",
      "Processed KIRP with 281 samples\n",
      "Removed 50029 (of 264786) mutations because they are silent\n",
      "Left with 167053 SNVs after removing 47704 mutations in hyper-mutated samples\n",
      "Processed COAD with 381 samples\n",
      "Removed 37375 (of 181116) mutations because they are silent\n",
      "Left with 137321 SNVs after removing 6420 mutations in hyper-mutated samples\n",
      "Processed LUSC with 488 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (87,118) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 22272 (of 102309) mutations because they are silent\n",
      "Left with 75872 SNVs after removing 4165 mutations in hyper-mutated samples\n",
      "Processed HNSC with 504 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (88,98) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 1519 (of 10899) mutations because they are silent\n",
      "Left with 9380 SNVs after removing 0 mutations in hyper-mutated samples\n",
      "Processed THCA with 491 samples\n",
      "Removed 29267 (of 134513) mutations because they are silent\n",
      "Left with 103792 SNVs after removing 1454 mutations in hyper-mutated samples\n",
      "Processed BLCA with 411 samples\n",
      "Removed 4467 (of 26693) mutations because they are silent\n",
      "Left with 22226 SNVs after removing 0 mutations in hyper-mutated samples\n",
      "Processed KIRC with 336 samples\n",
      "Removed 6620 (of 29286) mutations because they are silent\n",
      "Left with 22666 SNVs after removing 0 mutations in hyper-mutated samples\n",
      "Processed PRAD with 495 samples\n"
     ]
    }
   ],
   "source": [
    "all_matrices = []\n",
    "ctypes = []\n",
    "for dname in os.listdir(snv_base_dir):\n",
    "    ctype_dir = os.path.join(snv_base_dir, dname)\n",
    "    if os.path.isdir(ctype_dir):\n",
    "        for maffile in os.listdir(ctype_dir):\n",
    "            if maffile.endswith('.maf.gz'):\n",
    "                gene_sample_matrix = get_gene_sample_matrix(os.path.join(ctype_dir, maffile),\n",
    "                                                            ultra_mutated_samples_path=ultra_mutated_samples_path,\n",
    "                                                            gene_lengths=exonic_gene_lengths if NORMALIZE_FOR_GENE_LENGTH else None\n",
    "                                                           )\n",
    "                all_matrices.append(gene_sample_matrix)\n",
    "                ctype = maffile.split('.')[1]\n",
    "                print (\"Processed {} with {} samples\".format(ctype, gene_sample_matrix.shape[1]))\n",
    "                ctypes.append(ctype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples with mutations per Cancer Type:\n",
      "READ: (15181, 135) Samples\n",
      "STAD: (18897, 437) Samples\n",
      "LUAD: (18540, 563) Samples\n",
      "CESC: (18445, 289) Samples\n",
      "ESCA: (14161, 184) Samples\n",
      "UCEC: (21117, 512) Samples\n",
      "BRCA: (18291, 977) Samples\n",
      "LIHC: (14984, 364) Samples\n",
      "KIRP: (10503, 281) Samples\n",
      "COAD: (19304, 381) Samples\n",
      "LUSC: (18316, 488) Samples\n",
      "HNSC: (16939, 504) Samples\n",
      "THCA: (5814, 491) Samples\n",
      "BLCA: (18066, 411) Samples\n",
      "KIRC: (10985, 336) Samples\n",
      "PRAD: (11114, 495) Samples\n"
     ]
    }
   ],
   "source": [
    "print (\"Samples with mutations per Cancer Type:\")\n",
    "for i in range(len(all_matrices)):\n",
    "    print (\"{}: {} Samples\".format(ctypes[i], all_matrices[i].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21668, 6848)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the big gene x sample matrix for all cancer types\n",
    "whole_gene_sample_matrix = all_matrices[0].join(all_matrices[1:], how='outer').fillna(0)\n",
    "#np.save('../../data/pancancer/TCGA/mutation/gene_sample_snvs.npy', whole_gene_sample_matrix)\n",
    "whole_gene_sample_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>READ</th>\n",
       "      <th>STAD</th>\n",
       "      <th>LUAD</th>\n",
       "      <th>CESC</th>\n",
       "      <th>ESCA</th>\n",
       "      <th>UCEC</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>LIHC</th>\n",
       "      <th>KIRP</th>\n",
       "      <th>COAD</th>\n",
       "      <th>LUSC</th>\n",
       "      <th>HNSC</th>\n",
       "      <th>THCA</th>\n",
       "      <th>BLCA</th>\n",
       "      <th>KIRC</th>\n",
       "      <th>PRAD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1BG</th>\n",
       "      <td>0.014768</td>\n",
       "      <td>0.018042</td>\n",
       "      <td>0.015867</td>\n",
       "      <td>0.010318</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050370</td>\n",
       "      <td>0.003070</td>\n",
       "      <td>0.008235</td>\n",
       "      <td>0.003556</td>\n",
       "      <td>0.028149</td>\n",
       "      <td>0.010209</td>\n",
       "      <td>0.015759</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019330</td>\n",
       "      <td>0.002974</td>\n",
       "      <td>0.002020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1CF</th>\n",
       "      <td>0.009073</td>\n",
       "      <td>0.019704</td>\n",
       "      <td>0.025842</td>\n",
       "      <td>0.014170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022003</td>\n",
       "      <td>0.008660</td>\n",
       "      <td>0.010204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014861</td>\n",
       "      <td>0.037904</td>\n",
       "      <td>0.015977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009195</td>\n",
       "      <td>0.008777</td>\n",
       "      <td>0.012484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>0.072417</td>\n",
       "      <td>0.061422</td>\n",
       "      <td>0.045522</td>\n",
       "      <td>0.016192</td>\n",
       "      <td>0.021601</td>\n",
       "      <td>0.099077</td>\n",
       "      <td>0.010138</td>\n",
       "      <td>0.010957</td>\n",
       "      <td>0.010651</td>\n",
       "      <td>0.064007</td>\n",
       "      <td>0.040682</td>\n",
       "      <td>0.011606</td>\n",
       "      <td>0.002035</td>\n",
       "      <td>0.033780</td>\n",
       "      <td>0.014811</td>\n",
       "      <td>0.005739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2ML1</th>\n",
       "      <td>0.045499</td>\n",
       "      <td>0.057340</td>\n",
       "      <td>0.041338</td>\n",
       "      <td>0.025815</td>\n",
       "      <td>0.005370</td>\n",
       "      <td>0.071714</td>\n",
       "      <td>0.010906</td>\n",
       "      <td>0.010917</td>\n",
       "      <td>0.003547</td>\n",
       "      <td>0.088186</td>\n",
       "      <td>0.040467</td>\n",
       "      <td>0.017498</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028382</td>\n",
       "      <td>0.005933</td>\n",
       "      <td>0.002014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GNT</th>\n",
       "      <td>0.021274</td>\n",
       "      <td>0.006779</td>\n",
       "      <td>0.015875</td>\n",
       "      <td>0.016438</td>\n",
       "      <td>0.005411</td>\n",
       "      <td>0.027960</td>\n",
       "      <td>0.001022</td>\n",
       "      <td>0.002744</td>\n",
       "      <td>0.003557</td>\n",
       "      <td>0.015467</td>\n",
       "      <td>0.026568</td>\n",
       "      <td>0.007912</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004850</td>\n",
       "      <td>0.002974</td>\n",
       "      <td>0.002020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           READ      STAD      LUAD      CESC      ESCA      UCEC      BRCA  \\\n",
       "A1BG   0.014768  0.018042  0.015867  0.010318  0.000000  0.050370  0.003070   \n",
       "A1CF   0.009073  0.019704  0.025842  0.014170  0.000000  0.022003  0.008660   \n",
       "A2M    0.072417  0.061422  0.045522  0.016192  0.021601  0.099077  0.010138   \n",
       "A2ML1  0.045499  0.057340  0.041338  0.025815  0.005370  0.071714  0.010906   \n",
       "A4GNT  0.021274  0.006779  0.015875  0.016438  0.005411  0.027960  0.001022   \n",
       "\n",
       "           LIHC      KIRP      COAD      LUSC      HNSC      THCA      BLCA  \\\n",
       "A1BG   0.008235  0.003556  0.028149  0.010209  0.015759  0.000000  0.019330   \n",
       "A1CF   0.010204  0.000000  0.014861  0.037904  0.015977  0.000000  0.009195   \n",
       "A2M    0.010957  0.010651  0.064007  0.040682  0.011606  0.002035  0.033780   \n",
       "A2ML1  0.010917  0.003547  0.088186  0.040467  0.017498  0.000000  0.028382   \n",
       "A4GNT  0.002744  0.003557  0.015467  0.026568  0.007912  0.000000  0.004850   \n",
       "\n",
       "           KIRC      PRAD  \n",
       "A1BG   0.002974  0.002020  \n",
       "A1CF   0.008777  0.012484  \n",
       "A2M    0.014811  0.005739  \n",
       "A2ML1  0.005933  0.002014  \n",
       "A4GNT  0.002974  0.002020  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the mean matrices\n",
    "mean_mutations = []\n",
    "for df in all_matrices:\n",
    "    mean_mutations.append(df.mean(axis=1))\n",
    "mean_mutation_matrix = pd.DataFrame(mean_mutations, index=ctypes).T\n",
    "mean_mutation_matrix.fillna(0, inplace=True)\n",
    "mean_mutation_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TP53            0.405255\n",
       "TTN             0.365538\n",
       "MUC16           0.288166\n",
       "CSMD3           0.197950\n",
       "APC             0.195535\n",
       "                  ...   \n",
       "OR4A21P         0.000064\n",
       "OR2V1           0.000064\n",
       "NUTM2B          0.000064\n",
       "STARD4-AS1      0.000062\n",
       "RP11-553L6.5    0.000042\n",
       "Length: 21668, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_mutation_matrix.mean(axis=1).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mutation Frequency')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quick and dirty histogram\n",
    "number_to_display = 1000\n",
    "x = mean_mutation_matrix.mean(axis=1).sort_values(ascending=False)\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "plt.bar(x=range(number_to_display), height=x[:number_to_display])\n",
    "#_ = plt.hist(x, bins=np.linspace(0.0008, .2, 300))\n",
    "plt.xlabel('Top {} Mutated Genes'.format(number_to_display), fontsize=25)\n",
    "plt.ylabel('Mutation Frequency', fontsize=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "length_and_mean_matrix = mean_mutation_matrix.join(exonic_gene_lengths)\n",
    "fig = plt.figure(figsize=(30, 30))\n",
    "x, y = bestSplit(mean_mutation_matrix.shape[1])\n",
    "for i in range(mean_mutation_matrix.shape[1]):\n",
    "    plt.subplot(x, y, i+1)\n",
    "    sns.scatterplot(length_and_mean_matrix.iloc[:, i], length_and_mean_matrix.length)\n",
    "    plt.title('{0} (R={1:.2f})'.format(mean_mutation_matrix.columns[i],\n",
    "                                 length_and_mean_matrix.iloc[:, i].corr(length_and_mean_matrix.length)))\n",
    "#fig.savefig('../../data/pancancer/TCGA/mutation/mutation_length_correlation_raw.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_mutation_matrix.to_csv('../../data/pancancer/TCGA/mutation/gene_ctype_snvs.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "whole_gene_sample_matrix.columns = whole_gene_sample_matrix.columns.map(trim_fun)\n",
    "whole_gene_sample_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing CNAs\n",
    "This section loads the CNA mean matrix computed by `preprocess_cnas.ipynb` and incorporates that additional information into our model.\n",
    "The CNAs are also preprocessed a little. This involves the following steps:\n",
    "1. Load the CNA files\n",
    "2. Remove ultra-mutated samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed PRAD\n",
      "Processed LIHC\n",
      "Processed LUSC\n",
      "Processed BLCA\n",
      "Processed CESC\n",
      "Processed KIRP\n",
      "Processed BRCA\n",
      "Processed STAD\n",
      "Processed HNSC\n",
      "Processed READ\n",
      "Processed UCEC\n",
      "Processed LUAD\n",
      "Processed THCA\n",
      "Processed COAD\n",
      "Processed KIRC\n",
      "Processed ESCA\n"
     ]
    }
   ],
   "source": [
    "def process_cna_for_ctype(fname):\n",
    "    # load data for cancer type\n",
    "    gene_sample_matrix = pd.read_csv(fname, sep='\\t')\n",
    "    # remove the isoform from ensembl IDs\n",
    "    gene_sample_matrix.set_index(gene_sample_matrix['Gene Symbol'].str.split('.').str[0], inplace=True)\n",
    "    # drop uninformative columns\n",
    "    gene_sample_matrix.drop(['Gene Symbol', 'Gene ID', 'Cytoband'], axis=1, inplace=True)\n",
    "    # compute average of absolute CNAs (no cancelling out)\n",
    "    cna_mean_matrix = gene_sample_matrix.abs().mean(axis=1)\n",
    "    # get cancer type from file name\n",
    "    ctype = os.path.basename(fname).split('.')[0].strip()\n",
    "    # rename series to cancer type\n",
    "    cna_mean_matrix.rename(ctype, inplace=True)\n",
    "\n",
    "    return cna_mean_matrix, ctype\n",
    "\n",
    "cna_all_matrices = []\n",
    "cna_ctypes = []\n",
    "for dname in os.listdir(cna_base_dir):\n",
    "    ctype_dir = os.path.join(cna_base_dir, dname)\n",
    "    if os.path.isdir(ctype_dir):\n",
    "        for cna_gene_scores in os.listdir(ctype_dir):\n",
    "            if cna_gene_scores.endswith('focal_score_by_genes.txt'):\n",
    "                avg_cna, ctype = process_cna_for_ctype(os.path.join(ctype_dir, cna_gene_scores))\n",
    "                cna_all_matrices.append(avg_cna)\n",
    "                cna_ctypes.append(ctype)\n",
    "                print (\"Processed {}\".format(ctype))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRAD\t(19729,)\tTrue\n",
      "LIHC\t(19729,)\tFalse\n",
      "LUSC\t(19729,)\tFalse\n",
      "BLCA\t(19729,)\tFalse\n",
      "CESC\t(19729,)\tFalse\n",
      "KIRP\t(19729,)\tFalse\n",
      "BRCA\t(19729,)\tFalse\n",
      "STAD\t(19729,)\tFalse\n",
      "HNSC\t(19729,)\tFalse\n",
      "READ\t(19729,)\tFalse\n",
      "UCEC\t(19729,)\tFalse\n",
      "LUAD\t(19729,)\tFalse\n",
      "THCA\t(19729,)\tFalse\n",
      "COAD\t(19729,)\tFalse\n",
      "KIRC\t(19729,)\tFalse\n",
      "ESCA\t(19729,)\tFalse\n",
      "querying 1-1000...done.\n",
      "querying 1001-2000...done.\n",
      "querying 2001-3000...done.\n",
      "querying 3001-4000...done.\n",
      "querying 4001-5000...done.\n",
      "querying 5001-6000...done.\n",
      "querying 6001-7000...done.\n",
      "querying 7001-8000...done.\n",
      "querying 8001-9000...done.\n",
      "querying 9001-10000...done.\n",
      "querying 10001-11000...done.\n",
      "querying 11001-12000...done.\n",
      "querying 12001-13000...done.\n",
      "querying 13001-14000...done.\n",
      "querying 14001-15000...done.\n",
      "querying 15001-16000...done.\n",
      "querying 16001-17000...done.\n",
      "querying 17001-18000...done.\n",
      "querying 18001-19000...done.\n",
      "querying 19001-19729...done.\n",
      "Finished.\n",
      "270 input query terms found no hit:\n",
      "\t['ENSG00000273547', 'ENSG00000278566', 'ENSG00000279244', 'ENSG00000237847', 'ENSG00000268991', 'ENS\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(cna_all_matrices)):\n",
    "    print (\"{}\\t{}\\t{}\".format(cna_ctypes[i], cna_all_matrices[i].shape, (cna_all_matrices[i].index == cna_all_matrices[0].index).all()))\n",
    "cna_mean_matrix = reduce(lambda left, right: pd.merge(left, right, left_index=True, right_index=True), cna_all_matrices)\n",
    "# get gene symbols\n",
    "ensembl_symbol_map = utils.get_symbols_from_ensembl(cna_mean_matrix.index)\n",
    "cna_mean_with_names = cna_mean_matrix.join(ensembl_symbol_map)\n",
    "cna_mean_with_names.dropna(axis=0, inplace=True)\n",
    "cna_mean_with_names.set_index('Symbol', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Plot distributions\n",
    "We now have obtained two gene-sample matrices $S_{SNV} \\in \\mathbb{R}^{N \\times M_1}$ and $S_{CNA} \\in \\mathbb{R}^{N \\times M_2}$, containing mutation frequencies for genes $N$ per sample ($M_1$ for SNVs and $M_2$ for CNAs).\n",
    "\n",
    "Next, I want to plot some distributions and add the two together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1, 0.4)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 8))\n",
    "for col in mean_mutation_matrix.columns:\n",
    "    sns.kdeplot(mean_mutation_matrix[col], bw=0.005)\n",
    "plt.xlim([-0.1, .4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 8))\n",
    "for col in cna_mean_matrix.columns:\n",
    "    sns.kdeplot(cna_mean_matrix[col], bw=0.005)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Preprocess both together\n",
    "As next step, let's **remove samples that have a very high number of mutations when adding CNAs and SNVs** and afterwords **remove all samples that only have CNAs but no SNVs.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLCA</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>CESC</th>\n",
       "      <th>COAD</th>\n",
       "      <th>ESCA</th>\n",
       "      <th>HNSC</th>\n",
       "      <th>KIRC</th>\n",
       "      <th>KIRP</th>\n",
       "      <th>LIHC</th>\n",
       "      <th>LUAD</th>\n",
       "      <th>LUSC</th>\n",
       "      <th>PRAD</th>\n",
       "      <th>READ</th>\n",
       "      <th>STAD</th>\n",
       "      <th>THCA</th>\n",
       "      <th>UCEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1BG</th>\n",
       "      <td>0.156679</td>\n",
       "      <td>0.122419</td>\n",
       "      <td>0.107961</td>\n",
       "      <td>0.081509</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.105113</td>\n",
       "      <td>0.008068</td>\n",
       "      <td>0.016758</td>\n",
       "      <td>0.074198</td>\n",
       "      <td>0.102354</td>\n",
       "      <td>0.164789</td>\n",
       "      <td>0.015964</td>\n",
       "      <td>0.079474</td>\n",
       "      <td>0.115769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.145260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1CF</th>\n",
       "      <td>0.110400</td>\n",
       "      <td>0.075567</td>\n",
       "      <td>0.051207</td>\n",
       "      <td>0.056363</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>0.055901</td>\n",
       "      <td>0.015569</td>\n",
       "      <td>0.009901</td>\n",
       "      <td>0.044505</td>\n",
       "      <td>0.078095</td>\n",
       "      <td>0.100881</td>\n",
       "      <td>0.042365</td>\n",
       "      <td>0.038484</td>\n",
       "      <td>0.076522</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.073098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>0.110888</td>\n",
       "      <td>0.099650</td>\n",
       "      <td>0.053229</td>\n",
       "      <td>0.101556</td>\n",
       "      <td>0.097277</td>\n",
       "      <td>0.078146</td>\n",
       "      <td>0.023300</td>\n",
       "      <td>0.020552</td>\n",
       "      <td>0.069004</td>\n",
       "      <td>0.150026</td>\n",
       "      <td>0.138010</td>\n",
       "      <td>0.077452</td>\n",
       "      <td>0.143006</td>\n",
       "      <td>0.161422</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.139223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2ML1</th>\n",
       "      <td>0.105491</td>\n",
       "      <td>0.103131</td>\n",
       "      <td>0.059485</td>\n",
       "      <td>0.125736</td>\n",
       "      <td>0.081045</td>\n",
       "      <td>0.084038</td>\n",
       "      <td>0.014422</td>\n",
       "      <td>0.013448</td>\n",
       "      <td>0.066326</td>\n",
       "      <td>0.145842</td>\n",
       "      <td>0.141612</td>\n",
       "      <td>0.073727</td>\n",
       "      <td>0.116087</td>\n",
       "      <td>0.157340</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.113685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3GALT2</th>\n",
       "      <td>0.074710</td>\n",
       "      <td>0.115732</td>\n",
       "      <td>0.074197</td>\n",
       "      <td>0.092036</td>\n",
       "      <td>0.091913</td>\n",
       "      <td>0.064639</td>\n",
       "      <td>0.035654</td>\n",
       "      <td>0.039604</td>\n",
       "      <td>0.145119</td>\n",
       "      <td>0.100861</td>\n",
       "      <td>0.120367</td>\n",
       "      <td>0.025896</td>\n",
       "      <td>0.105882</td>\n",
       "      <td>0.081826</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.087699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             BLCA      BRCA      CESC      COAD      ESCA      HNSC      KIRC  \\\n",
       "A1BG     0.156679  0.122419  0.107961  0.081509  0.200000  0.105113  0.008068   \n",
       "A1CF     0.110400  0.075567  0.051207  0.056363  0.108108  0.055901  0.015569   \n",
       "A2M      0.110888  0.099650  0.053229  0.101556  0.097277  0.078146  0.023300   \n",
       "A2ML1    0.105491  0.103131  0.059485  0.125736  0.081045  0.084038  0.014422   \n",
       "A3GALT2  0.074710  0.115732  0.074197  0.092036  0.091913  0.064639  0.035654   \n",
       "\n",
       "             KIRP      LIHC      LUAD      LUSC      PRAD      READ      STAD  \\\n",
       "A1BG     0.016758  0.074198  0.102354  0.164789  0.015964  0.079474  0.115769   \n",
       "A1CF     0.009901  0.044505  0.078095  0.100881  0.042365  0.038484  0.076522   \n",
       "A2M      0.020552  0.069004  0.150026  0.138010  0.077452  0.143006  0.161422   \n",
       "A2ML1    0.013448  0.066326  0.145842  0.141612  0.073727  0.116087  0.157340   \n",
       "A3GALT2  0.039604  0.145119  0.100861  0.120367  0.025896  0.105882  0.081826   \n",
       "\n",
       "             THCA      UCEC  \n",
       "A1BG     0.000000  0.145260  \n",
       "A1CF     0.009766  0.073098  \n",
       "A2M      0.003988  0.139223  \n",
       "A2ML1    0.001953  0.113685  \n",
       "A3GALT2  0.001953  0.087699  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_genes = pd.merge(cna_mean_with_names, mean_mutation_matrix, left_index=True, right_index=True, how='inner').index\n",
    "snv_cna_matrix = mean_mutation_matrix[mean_mutation_matrix.index.isin(common_genes)] + cna_mean_with_names[cna_mean_with_names.index.isin(common_genes)]\n",
    "snv_cna_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 8))\n",
    "for col in snv_cna_matrix.columns:\n",
    "    sns.kdeplot(snv_cna_matrix[col], bw=0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17920,), (17923, 16))"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snv_cna_matrix.index.drop_duplicates().shape, snv_cna_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "snv_cna_matrix = snv_cna_matrix.reset_index().drop_duplicates(subset='index').set_index('index')\n",
    "snv_cna_matrix.to_csv('../../data/pancancer/TCGA/mutation/mutfreq_mean.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "if USE_CNAS:\n",
    "    final_matrix = cna_snv_sample_mat\n",
    "else:\n",
    "    final_matrix = snv_gene_sample_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Convert Gene Names to Ensembl IDs\n",
    "I finally have one big gene $\\times$ sample matrix. I next want to get ensembl IDs for each of the genes to be able to compare with nodes in the networks afterwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "querying 1-1000...done.\n",
      "querying 1001-2000...done.\n",
      "querying 2001-3000...done.\n",
      "querying 3001-4000...done.\n",
      "querying 4001-5000...done.\n",
      "querying 5001-6000...done.\n",
      "querying 6001-7000...done.\n",
      "querying 7001-8000...done.\n",
      "querying 8001-9000...done.\n",
      "querying 9001-10000...done.\n",
      "querying 10001-11000...done.\n",
      "querying 11001-12000...done.\n",
      "querying 12001-13000...done.\n",
      "querying 13001-14000...done.\n",
      "querying 14001-15000...done.\n",
      "querying 15001-16000...done.\n",
      "querying 16001-17000...done.\n",
      "querying 17001-18000...done.\n",
      "querying 18001-19000...done.\n",
      "querying 19001-20000...done.\n",
      "querying 20001-21000...done.\n",
      "querying 21001-21668...done.\n",
      "Finished.\n",
      "1764 input query terms found dup hits:\n",
      "\t[('AACSP1', 2), ('AADACL2-AS1', 3), ('AATBC', 2), ('ABCA17P', 2), ('ABCC13', 2), ('ABCC6P1', 2), ('A\n",
      "1577 input query terms found no hit:\n",
      "\t['AAED1', 'AATK-AS1', 'AC000036.4', 'AC002519.6', 'AC003002.6', 'AC004076.7', 'AC004076.9', 'AC00415\n",
      "1618 (of 21668) genes had no mapping Ensembl ID\n"
     ]
    }
   ],
   "source": [
    "# use mygene to get ensembl IDs from Hugo Symbols\n",
    "mg = mygene.MyGeneInfo()\n",
    "res = mg.querymany(final_matrix.index,\n",
    "                   scopes='symbol',\n",
    "                   fields='ensembl.gene, symbol',\n",
    "                   species='human', returnall=True\n",
    "                  )\n",
    "\n",
    "# now, retrieve the names and IDs from a dictionary and put in DF\n",
    "def get_name_and_id(x):\n",
    "    ens_id = x['ensembl'][0]['gene'] if type(x['ensembl']) is list else x['ensembl']['gene']\n",
    "    name = x['symbol']\n",
    "    query = x['query']\n",
    "    if not name == query:\n",
    "        print (\"Error: \", name, query)\n",
    "    return [ens_id, name]\n",
    "\n",
    "ens_ids = [get_name_and_id(x) for x in res['out'] if 'ensembl' in x]\n",
    "mapping = pd.DataFrame(ens_ids, columns=['Ensembl_ID', 'Name'])\n",
    "mapping.set_index('Name', inplace=True)\n",
    "mapping = mapping[~mapping.index.duplicated(keep='first')]\n",
    "\n",
    "# join mutation frequencies with our derived mapping\n",
    "mut_freq_sample = final_matrix.join(mapping)\n",
    "print (\"{} (of {}) genes had no mapping Ensembl ID\".format(mut_freq_sample.Ensembl_ID.isnull().sum(),\n",
    "                                                           mut_freq_sample.shape[0]))\n",
    "mut_freq_sample.dropna(inplace=True) # remove unmapped genes simply\n",
    "\n",
    "# make Ensembl ID the index and move Hugo Symbols to column\n",
    "mut_freq_sample['Name'] = mut_freq_sample.index\n",
    "mut_freq_sample.set_index('Ensembl_ID', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "271486.11787159997"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mut_freq_sample.drop('Name', axis=1).sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Mean across cancer types & write to file\n",
    "Finally, I want to compute the mean mutation frequency per cancer type and write the resulting $Gene \\times Ctype$ matrix to a _csv_ file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TCGA-AF-3914</th>\n",
       "      <th>TCGA-AF-5654</th>\n",
       "      <th>TCGA-AF-6655</th>\n",
       "      <th>TCGA-AG-3878</th>\n",
       "      <th>TCGA-AG-3891</th>\n",
       "      <th>TCGA-AG-3894</th>\n",
       "      <th>TCGA-AG-3902</th>\n",
       "      <th>TCGA-AG-3906</th>\n",
       "      <th>TCGA-AG-4009</th>\n",
       "      <th>TCGA-AG-4022</th>\n",
       "      <th>...</th>\n",
       "      <th>TCGA-ZG-A8QX</th>\n",
       "      <th>TCGA-ZG-A8QY</th>\n",
       "      <th>TCGA-ZG-A8QZ</th>\n",
       "      <th>TCGA-ZG-A9L2</th>\n",
       "      <th>TCGA-ZG-A9L4</th>\n",
       "      <th>TCGA-ZG-A9L6</th>\n",
       "      <th>TCGA-ZG-A9LN</th>\n",
       "      <th>TCGA-ZG-A9MC</th>\n",
       "      <th>TCGA-ZG-A9ND</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000121410</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.994532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A1BG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000148584</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A1CF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000175899</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000166535</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A2ML1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000184389</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A3GALT2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3055 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TCGA-AF-3914  TCGA-AF-5654  TCGA-AF-6655  TCGA-AG-3878  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSG00000121410           0.0           0.0           0.0           0.0   \n",
       "ENSG00000148584           0.0           0.0           0.0           0.0   \n",
       "ENSG00000175899           0.0           0.0           0.0           0.0   \n",
       "ENSG00000166535           0.0           0.0           0.0           0.0   \n",
       "ENSG00000184389           0.0           0.0           0.0           0.0   \n",
       "\n",
       "                 TCGA-AG-3891  TCGA-AG-3894  TCGA-AG-3902  TCGA-AG-3906  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSG00000121410           0.0           0.0           0.0      0.994532   \n",
       "ENSG00000148584           0.0           0.0           0.0      0.000000   \n",
       "ENSG00000175899           0.0           0.0           0.0      0.000000   \n",
       "ENSG00000166535           0.0           0.0           0.0      0.000000   \n",
       "ENSG00000184389           0.0           0.0           0.0      0.000000   \n",
       "\n",
       "                 TCGA-AG-4009  TCGA-AG-4022   ...     TCGA-ZG-A8QX  \\\n",
       "Ensembl_ID                                    ...                    \n",
       "ENSG00000121410           0.0           0.0   ...              0.0   \n",
       "ENSG00000148584           0.0           0.0   ...              0.0   \n",
       "ENSG00000175899           0.0           0.0   ...              0.0   \n",
       "ENSG00000166535           0.0           0.0   ...              0.0   \n",
       "ENSG00000184389           0.0           0.0   ...              0.0   \n",
       "\n",
       "                 TCGA-ZG-A8QY  TCGA-ZG-A8QZ  TCGA-ZG-A9L2  TCGA-ZG-A9L4  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSG00000121410           0.0           0.0           0.0           0.0   \n",
       "ENSG00000148584           0.0           0.0           0.0           0.0   \n",
       "ENSG00000175899           0.0           0.0           0.0           0.0   \n",
       "ENSG00000166535           0.0           0.0           0.0           0.0   \n",
       "ENSG00000184389           0.0           0.0           0.0           0.0   \n",
       "\n",
       "                 TCGA-ZG-A9L6  TCGA-ZG-A9LN  TCGA-ZG-A9MC  TCGA-ZG-A9ND  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSG00000121410           0.0           0.0           0.0           0.0   \n",
       "ENSG00000148584           0.0           0.0           0.0           0.0   \n",
       "ENSG00000175899           0.0           0.0           0.0           0.0   \n",
       "ENSG00000166535           0.0           0.0           0.0           0.0   \n",
       "ENSG00000184389           0.0           0.0           0.0           0.0   \n",
       "\n",
       "                    Name  \n",
       "Ensembl_ID                \n",
       "ENSG00000121410     A1BG  \n",
       "ENSG00000148584     A1CF  \n",
       "ENSG00000175899      A2M  \n",
       "ENSG00000166535    A2ML1  \n",
       "ENSG00000184389  A3GALT2  \n",
       "\n",
       "[5 rows x 3055 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submitter_project_mapping = pd.read_json('../../data/pancancer/TCGA/mutation/download_new/cases_all_cancers.2018-11-26.json')\n",
    "submitter_project_mapping.head()\n",
    "submitter_project_mapping['Cancer_Type'] = [dict(i)['project_id'] for i in submitter_project_mapping.project]\n",
    "submitter_project_mapping = submitter_project_mapping[['submitter_id', 'Cancer_Type']].set_index('submitter_id')\n",
    "submitter_project_mapping.head()\n",
    "\n",
    "trim_to_submitter = lambda x: '-'.join(str(x).split('-')[:3]) # TCGA barcode until patient\n",
    "mut_freq_sample.columns = mut_freq_sample.columns.map(trim_to_submitter)\n",
    "mut_freq_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1839.6552506103312\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLCA</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>CESC</th>\n",
       "      <th>COAD</th>\n",
       "      <th>ESCA</th>\n",
       "      <th>HNSC</th>\n",
       "      <th>KIRC</th>\n",
       "      <th>KIRP</th>\n",
       "      <th>LIHC</th>\n",
       "      <th>LUAD</th>\n",
       "      <th>LUSC</th>\n",
       "      <th>PRAD</th>\n",
       "      <th>READ</th>\n",
       "      <th>STAD</th>\n",
       "      <th>THCA</th>\n",
       "      <th>UCEC</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.005490</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008632</td>\n",
       "      <td>A1BG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000148584</th>\n",
       "      <td>0.010427</td>\n",
       "      <td>0.012267</td>\n",
       "      <td>0.009366</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003478</td>\n",
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       "      <td>0.042437</td>\n",
       "      <td>0.004815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011474</td>\n",
       "      <td>A1CF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000175899</th>\n",
       "      <td>0.011217</td>\n",
       "      <td>0.004165</td>\n",
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       "      <td>0.016402</td>\n",
       "      <td>0.002167</td>\n",
       "      <td>0.021486</td>\n",
       "      <td>A2M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000166535</th>\n",
       "      <td>0.022307</td>\n",
       "      <td>0.006229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060887</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005302</td>\n",
       "      <td>0.017576</td>\n",
       "      <td>0.015081</td>\n",
       "      <td>0.026799</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026814</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008506</td>\n",
       "      <td>A2ML1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000184389</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012251</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005477</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>A3GALT2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BLCA      BRCA      CESC      COAD      ESCA      HNSC  \\\n",
       "ENSG00000121410  0.000000  0.004174  0.000000  0.006164  0.000000  0.003504   \n",
       "ENSG00000148584  0.010427  0.012267  0.009366  0.000000  0.000000  0.003478   \n",
       "ENSG00000175899  0.011217  0.004165  0.000000  0.018338  0.022108  0.010508   \n",
       "ENSG00000166535  0.022307  0.006229  0.000000  0.060887  0.000000  0.024803   \n",
       "ENSG00000184389  0.000000  0.000000  0.012251  0.000000  0.000000  0.000000   \n",
       "\n",
       "                     KIRC      KIRP      LIHC      LUAD      LUSC      PRAD  \\\n",
       "ENSG00000121410  0.006366  0.005316  0.017685  0.000000  0.000000  0.002512   \n",
       "ENSG00000148584  0.006296  0.000000  0.008706  0.044433  0.000000  0.009964   \n",
       "ENSG00000175899  0.006365  0.005304  0.000000  0.046101  0.053943  0.002512   \n",
       "ENSG00000166535  0.000000  0.005302  0.017576  0.015081  0.026799  0.002505   \n",
       "ENSG00000184389  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "                     READ      STAD      THCA      UCEC     Name  \n",
       "ENSG00000121410  0.043241  0.005490  0.000000  0.008632     A1BG  \n",
       "ENSG00000148584  0.042437  0.004815  0.000000  0.011474     A1CF  \n",
       "ENSG00000175899  0.000000  0.016402  0.002167  0.021486      A2M  \n",
       "ENSG00000166535  0.000000  0.026814  0.000000  0.008506    A2ML1  \n",
       "ENSG00000184389  0.000000  0.005477  0.000000  0.000000  A3GALT2  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join with mapping and compute the mean across cancer types\n",
    "no_names = mut_freq_sample.drop('Name', axis=1)\n",
    "matrix_with_cancer_types = no_names.T.join(submitter_project_mapping)\n",
    "mean_mut_freqs = matrix_with_cancer_types.groupby('Cancer_Type').mean().T\n",
    "print (mean_mut_freqs.sum().sum())\n",
    "mean_mut_freqs.columns = [i.split('-')[1] for i in mean_mut_freqs.columns]\n",
    "\n",
    "# get back the gene names\n",
    "assert((mean_mut_freqs.index == mut_freq_sample.index).all())\n",
    "mean_mut_freqs['Name'] = mut_freq_sample.Name\n",
    "\n",
    "# write to disk\n",
    "mean_mut_freqs.to_csv('../../data/pancancer/TCGA/mutation/{}_mean.tsv'.format('mutfreq' if USE_CNAS else 'snvs'),\n",
    "                      sep='\\t')\n",
    "\n",
    "mean_mut_freqs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLCA</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>CESC</th>\n",
       "      <th>COAD</th>\n",
       "      <th>ESCA</th>\n",
       "      <th>HNSC</th>\n",
       "      <th>KIRC</th>\n",
       "      <th>KIRP</th>\n",
       "      <th>LIHC</th>\n",
       "      <th>LUAD</th>\n",
       "      <th>LUSC</th>\n",
       "      <th>PRAD</th>\n",
       "      <th>READ</th>\n",
       "      <th>STAD</th>\n",
       "      <th>THCA</th>\n",
       "      <th>UCEC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TP53</th>\n",
       "      <td>0.195622</td>\n",
       "      <td>0.176912</td>\n",
       "      <td>0.035629</td>\n",
       "      <td>0.450316</td>\n",
       "      <td>0.817965</td>\n",
       "      <td>0.648518</td>\n",
       "      <td>0.012513</td>\n",
       "      <td>0.005277</td>\n",
       "      <td>0.093728</td>\n",
       "      <td>0.316691</td>\n",
       "      <td>0.491423</td>\n",
       "      <td>0.072238</td>\n",
       "      <td>0.545195</td>\n",
       "      <td>0.345134</td>\n",
       "      <td>0.002157</td>\n",
       "      <td>0.074704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          BLCA      BRCA      CESC      COAD      ESCA      HNSC      KIRC  \\\n",
       "Name                                                                         \n",
       "TP53  0.195622  0.176912  0.035629  0.450316  0.817965  0.648518  0.012513   \n",
       "\n",
       "          KIRP      LIHC      LUAD      LUSC      PRAD      READ      STAD  \\\n",
       "Name                                                                         \n",
       "TP53  0.005277  0.093728  0.316691  0.491423  0.072238  0.545195  0.345134   \n",
       "\n",
       "          THCA      UCEC  \n",
       "Name                      \n",
       "TP53  0.002157  0.074704  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = mean_mut_freqs.set_index('Name')\n",
    "t[t.index == 'TP53']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLCA     KMT2D\n",
       "BRCA    PIK3CA\n",
       "CESC    PIK3CA\n",
       "COAD       APC\n",
       "ESCA      TP53\n",
       "HNSC      TP53\n",
       "KIRC       VHL\n",
       "KIRP       TTN\n",
       "LIHC    CTNNB1\n",
       "LUAD     MUC16\n",
       "LUSC      TP53\n",
       "PRAD      SPOP\n",
       "READ       APC\n",
       "STAD       TTN\n",
       "THCA      BRAF\n",
       "UCEC      PTEN\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_mut_freqs.set_index('Name').idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot information on the collected mutation frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6f5f4390>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mr44cORKXL1+Ohx56qOxWeiI8AgbChRVgcMxqbG9qairOnj0bjzzySNmtAFuE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\nRBIpeSQiIiIiIiIiIon+H6NrggRtKxdrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "sns.boxplot(x='variable', y='value', data=pd.melt(mean_mut_freqs, id_vars='Name'))\n",
    "#plt.ylim([-0.01, 0.1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Mutation Frequency')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8efPmbcO9AQ+m+w9/tdtldOd+fJ1ueOpv0Q2vfNsRj30f3aFIRXf43LNa+1Wt7cd0I2d9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\niIhoQUJURERERERECxKiIiIiIiIiWvAPFv5vT540fn0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = mean_mut_freqs.sum(axis=1).plot(kind='hist', bins=np.linspace(0, 2, 200), figsize=(14, 8))\n",
    "plt.xlim([0, 2])\n",
    "ax.set_ylabel('Number of Genes', fontsize=15)\n",
    "ax.set_title('Distribution of both Mutation Frequencies', fontsize=20)\n",
    "ax.set_xlabel('Mutation Frequency', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Write a HDF container with the \"raw\" mutation frequencies\n",
    "In the HDF5 container, I want to write the following:\n",
    "\n",
    "* $Gene \\times Sample$ matrix (for both, CNAs and SNVs)\n",
    "* CNA matrix ($Gene \\times Samples$)\n",
    "* SNV matrix ($Gene \\times Samples$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw information written to ../../data/pancancer/TCGA/mutation/mutation_frequencies_lengthnormalized.h5\n"
     ]
    }
   ],
   "source": [
    "fname = '../../data/pancancer/TCGA/mutation/mutation_frequencies_{}.h5'.format('lengthnormalized' if NORMALIZE_FOR_GENE_LENGTH else 'raw')\n",
    "snv_gene_sample_matrix.to_hdf(fname, 'snv_sample_matrix', mode='a')\n",
    "final_cna_matrix.to_hdf(fname, 'cna_sample_matrix', mode='a')\n",
    "cna_snv_sample_mat.to_hdf(fname, 'snv_cna_sample_matrix', mode='a')\n",
    "print (\"Raw information written to {}\".format(fname))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## T-SNE plot of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sasse/.local/lib/python3.6/site-packages/umap/spectral.py:229: UserWarning: Embedding a total of 3 separate connected components using meta-embedding (experimental)\n",
      "  n_components\n"
     ]
    }
   ],
   "source": [
    "mut_sample_mat = matrix_with_cancer_types.set_index('Cancer_Type')\n",
    "embedding = umap.UMAP().fit_transform(mut_sample_mat.drop(['Cancer_Type'], axis=1, errors='ignore'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2/NTlsFVYD5RSq6HHygWAP4vIjYdlFmkG/WOE5+3h/tqy2hxzADhXROxDnFjj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\n/mI+E559ClMbE3fOuYcGCfUZ2vp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so6oqeXl5f/q1+vbt6+jcuXNRRW2SkpICX375Zdqv\nv/6a/fDDD1dZv359qMvlkseMGVMzLi5uf//+/UsgWEmalpZmKigouKTP5O/s71BRGqppWgdgFPDG\nyW2PAGs0TfsAGCZJ0iX9dED4/TRVI/9IgLT1XgrSKq/szt3vJ7JG6fxe0kHNDhaqtDShNwZfstZI\nHfV6WgkENPRGidj6BsKSdehMEknNTFjC/6J/8x4FZXvF8zF7vzyKWk6QerbA+lwCPh/ayVWpxjw2\nHW1jMcq2AnyLTpQKSQGUfSW4nt9N40G3I+uDz7D2rUNZcGBpmedvVqUxvoCPX8YvZWT74XSu25Gb\nrxrEF6Pm0qtRdyYsmMQ3935RqprzbLIMcZEQHwXxkaVD0s1bNpcKSc927NgxfA4fE3s/XmGY/VDX\n+6gSkcTh3CPEhETTMKE+jRIbUCO6GjGh0YRZLyzQTAiLp2pUMolhCQxpdSPzRn/Mlom/sHjcfG6M\nrMehOZO49pEPqN//fiwR8aUqcgVBEARBEARBEIS/lmTRKeik3zfRqF7SJIuu4oqtP8hqtWq1a9d2\nA6SlpZW5+vwfVadOnQtadbhNmzbudevWHXjnnXeOmM1mVVVVnnvuudPDTJs3b+4EyMrKMnq9XjHv\nXBmu+IpSTdMWnPx2E5Bx8vs+wIcnv88GWgMrLnPXhEqoiobilcg/4sfrUAlNMFR6jCVSpkFfK4nN\nTGTu8WEJlUm+2oQxRMYUUjrAskbqqBqpI66ekYBPQ2+SzmtzuWnFlQ8pV4+7kfQX0E9ZQm8wYbaG\nEJNYlbCSUPQtwnFNKHsFeAAty4P6WwmN77yL/fPnUeeeu7n34+Flth3T8W7GfvIgxZ4S3h76JjH2\nGA5nH+G39N20qt6S78bNJ9Yec3GVuQEvASVA+vH0Cpv9tmsXV7VrxfQhbzJ+/lO4fGfmuzbpTUzq\n+yRtarQmMTyBxPDyq0YvRqjFDtiJsQd/dhdmoVRrRMJdrTBYbOhN1j/lOoIgCIIgCIIgCMKfR1c/\n1IlO0n5XVaksabp6oZdsftJTbrjhhvxXX301afPmzfbi4mI5NDT0Tx3aPn369PP+yF62bJnt0KFD\nxtGjR583XHXs2LH5brdbfvTRR6vt2bPn9B+7Q4cOzZ8zZ06s2+2WlyxZEjJw4MAy5gP8d7uiSqck\nSXpKkqQ553wNPLm7D/DKye+jgVMvBA9Q5pLWkiSNliRpsyRJm3Nyci5t54Xz+FwqAZ9G3qEAMbUN\nRNYwYLBU/L7WcpgdW5SOpBYmrhpmp+H1Nuzx+goDUJNdRmeQCHg1vI6/eJoNS+WVrFKoHskiV7pm\nkO/qGHalR9D5hjuoVq8p+rQA6lHXeZWk5/KvyKFe35u5edkaXt74PiVex3ltJvd/hqTwJL4YPZe5\nI2YRUBRMOiNta7bmoW7juLZ2e+JCYysNSRVFJTO7hNTjhRxOy8cdUPH6vFS2qmBhYSFGycCaA2v5\n5t4veXfYNCb0eox3hk5h44RV3NzqRpL+pIC0PJbwOEJiqmIJjxEhqSAIgiAIgiAIwhVK1zzcKdn1\nv2vxISlU79c1D7/kQeljjz2WExMT4/f5fNLLL78cW1l7l8slPf744xf9R6/L5ZI+//zz08MrlyxZ\nUu5Qy5EjR+YD2O320xW13bt3d3bs2LEI4D//+U+ZWdq5xo8fn+Byuf411adXVFCqadpLmqbdec7X\nQkmSogGbpmmnxhDnAKeSDTtw/rLYwfPN1DStlaZprWJiYspqIlxCniKNgiN+9GaJKq1M7P3eSdt7\nQsttX7WdibAqOiT5wv79OfMVitIDHFju4puHcvnyrmy+n5BH+nYv7sJLWllfLsmqR06yVNjG0DcR\n7Hr015b/mpTsegrrR9H/aTO9hj1IREw8mlEG3wXcl08lJD6RfV9+zoTej/H6jS/Ssmpz6sXV5dar\nb2LxuPlUj6pOVEgEVSKSaFG1GV3qd6JmTA2iQiIv+F4Li90cTM3n8Zd+xuv1ER9rRlM1/Grl//8y\nmUzIsswXW+Yz6N2htKjanFHX3snNV91IlYgkIm0RlZ5DEARBEARBEARB+OeTJAnj9UkZGKWLq4wy\nSqrx+qTMy7F+SXR0tDJ79uwjJpNJmzp1asIPP/wQUl5br9crjRkzJvnee+/NPbVNPTnlXmVFR+PG\njasSHR19+o/uxYsXR65fv77MEGLLli0WgIEDB+afvX3u3LlpSUlJvl9++SV00qRJcWUde8pzzz0X\ne8011zisVuvvm/rgb+iKCkrLcnL+0T6apv1PkiS9JElRwPdA05NNkoD1f1kHhXIpHo3cQ36a3hjC\n2hlFNOxnw2iV6DYpgtCEM5WXBqvEVcND6PNiJPbYymeDUBWNvCN+nNkKSybk8c0DeRzb6CXvUIBD\nKzx8dls2K98oxF1w+cNSOdyI+b465f7LkhPMGK6JQbYaMI+uha7V+cGkFGnE+WQzRsww4nCB2xFB\n72H3Q0MrcrXKp+OVG9rZ9Pbr2Nq25O6P76dz3Y5Mvfk13r99OrVja3Hb7JHUjqn5u+4vEFDIzC7h\nwOE8MrMdjHh0IW8/353khBA8bg+5ubnkuwsIDw+v8DxVa1TD6Xdx9zV3sOrRH6geWZUwa9hfsgCX\nIAiCIAiCIAiCcGUz9k/Kk5NtLvQXOFepXtLkqjaX8fqkMgvrLoV+/fqVfP755wfsdrvSv3//uo88\n8kji0aNHS4UcS5YsCbnjjjuqjh8/Pqt69eqn5+7LysoyAOTk5JQ5Z6HL5ZIeeeSRxOXLl4d169bt\ndIWs3++XevfuXW/KlCnRDofj9B/Uv/76q+Xuu++u3rx5c+cbb7xx4uxzVatWzb9s2bJ9TZo0cU6e\nPLnKgAEDamzcuLFU2Lpz507TqFGjqtStW9f7bxuef0XPUSpJkglYDIRKkvQAEAa0BKYBz0iSVBOY\no2nav+qX9ndhCJGocpWZ7V+UUOc6K7sXOWl9VyhZe7z0ejESg0VCZ5Awh8noLBoGw4WFZK48hQPL\nXHiLNI5vLXtRpN0LXdS4xky9XtbLHr7p6tixvtwMzzsHUNNcJzdK6K+JxnxXTeQIIxAMVS0P10Mr\n9uNbk4PPoeCsHUGO3cbYdwz8ujfY7/wSCQ/h1IoOoB4qQVfXjrK/nJe8DGobM4Gteg6b3Tza/X50\nsswz3zzPqgO/AHBLi8FEWC6+YrOw2M0PKw7yyOQfWf75HSz8aR+LZ92ArJNxu91s2bKFsLAwQpJC\nqdO4DpvXbi7z0zCr1UqtWjXxKm6aJDUh1GxHr7ui34oEQRAEQRAEQRCEv5BkkDXbi00POCfurKMe\nc1rxaeUX/hklVa5qc9leaHpAMsiXtRJy4MCBJR06dNj91ltvRS9atCj8448/jjYajVqtWrXcISEh\nap8+fQo/+OCDoxaLRQNYsGBB6IoVK0K++uqrKIANGzbYa9So0chms52unvV4PHJ6errR4/HIjz/+\neKm5SseNG5fZokUL17Jly+z/+9//YgAcDofOZDKpQ4YMyXvmmWeyTl3rbPXq1fNt27YtZdasWZHz\n5s2LGDBgQG1FUaRatWp5QkJClLZt2zqee+65zMTExN815TO4HLUAACAASURBVMHfmVRZWe8/RatW\nrbTNmzf/1d34V/EUq6T96mH/jy6MNpnmt4Swb6mLyGp64hsaMdgkZL2EzgBGi4zeXHmBsxrQ2PRB\nMXW6W/l4SBY+R/mv3+g6BgbNiCY0/q8J4dRCH3gUtICGZNGBVYdsOb8vhcVQ6IInZsBvRyAlrfT+\nW67TmDRUJdauEqZXoMiP65W9aFnnLHqnkzA8XINsczrHjCWYIyLZk5HCw/OeACDSFsE97e7m9na3\nEhdxcVNRKIrK/CV7uWXsPK65uirTnu1FwYkM2nWoH7xXVSUzM5OCggKq1Enm041f0q9eLw7s2k9R\nUREQHC4RnxBPzYa1sFttbEzbStuaVxNpu/Dh/oIgCIIgCIIgCMKVRZKkLZqmtbqQtjt27Eht1qxZ\nbuUty6b5Vcm3KD3K9216glYS0KNqEgFNQi9pyJImher9xuuTMo3XJ+Vd7pBU+PvYsWNHdLNmzaqX\ntU+UcQmXjDlUJrGpEWu4zL4f3Xw5MpsaHSwYDBKFx1zoTRJNBoVgjah8AaRT3EUqh1Z5qHWdpcKQ\nFCD3gB/Fp5Ff4MIeYsJguPDr/BnkcOMFtfMpkJUP364Fn//8/V+tlpg8QuKh9w28eIdEXLiM7aWm\nBHYV4l+eDT4VXeMw9D1i8ehcGOUkaqCBTibKFsX68SsI+AJY9FYirRGERdgv+l5y8pw89OwPAISG\nmLBY9DTo0ABVUzl48CDHjh1D0zTi4uKwyVZubjWI5757mZuaD6RtTDuUgIJOr2ND6ibqGuti1Un0\natQdWb7iZ/8QBEEQBEEQBEEQrhCSQdZMg5JzjTdUyVW2F9qUfcU2za3oJItO0dUPdeqahTvFlG7C\nHyGCUuGSssfpkXUSDfpB40E2HFkKsg6iahswWCWs4eeHl16His+pkn84ABJE1giuem+0yaCBOVzm\nQgqhdUaQZfhlXTpbD6Rz15CWJMSGXHHhnNkIR7Ogdxv45pfz9ysKDH1J5sMJGre+omfsQGhQHax1\n4oltGI1O1jju1BFtBavRQIxBh14ffK4epxev24dslrFHBOc3DXi9eAoKCLicpK/7BWdmJlENGxHX\nvCWW6Gh0xvMD3rwCNxlZJeh0Mrf2b0i1RBsut4ulS5cSCJypxC8uLubQoUN0796dF/pP4kheKu+t\nn42MTJ/GPenW8DriQ2OCvxhBEARBEARBEARB+B0kSULfIsKpbxFxyVe0F/5dRFAqXHK2aB22aB3u\nQgV7vA6DRcJoKTsoc+UprHqzkD3fudBOrsUk66HxDTauvT8Mo00iupYe1QdxDQ1k7SmjBPOk+j0t\n6Dfn0Ck5kv2HsmjW413WLryLejWjy2zv9oLHB5oK4fbLl+WF2qBaHNw/GL5bHwxGz7U5BbYflJj1\nOHy9SmPaPIkwO9StouPOPhJ1koJ9htIhp9lmwmwznf7ZW1JC0ZFDpP78E+smT0Lxek/vM4WF0e+j\nL0i6pgMGq7XUeZwuH3ExISycNZi6NcNwuVysXLmyVEh6iqqqLF26lG7duqFk+Lmn2Qg0TcNgMBBu\nCRMhqSAIgiAIgiAIgiAIVySRWAiXjSVchy1SV2ZIqgY0vA6Fn57LY/c3Z0LS4D7YOc/J0skFBHwa\nDa8PQdJBu7FhUE5FvcEi0XaoFfXLVNRndzOsShVu69uIwfd8SXau43S7EhfkF8GJXMgtguPZkF0Y\n/G9OARdUufpnSIqBTXtgzgQIsZTep9PB40PB5YXmd8GOQxJjb4D/uxMmDpdoUO1USFq5kmNHyd6+\njTVPP1EqJAXwFhXx9Q19KUo9fN5xdRJlNn3Rn2oJRg4fOoTD4cB7zvFnUxSFrKwsvF4vGzZsIC0t\njaioKMwm84V1VBAEQRAEQRAEQRAE4TITQanwl/E6VIpOBFj/XhFLJuaz+UMHV98VRruxoWUGoPt/\nduMuULHHy3hKVECj76uRhMSWHr4fWUPPkOkR6OcfRisJVjyaPzrGw0OvZs/+HHLygivRH88+WUXq\nDwam6TnBkFQjuO23I3A85xI/hJOSYuC2npCZD8umwefPwvhb4ZUxsPotUFQY/ToEFJi/Cu5+Fa5/\nMlgBW3yBAw3ceXmUpB9j4xuvlNtGUxR+efZpvCcXYAJwZKex5vk+GCUvRpMJh8NBfn5+pdcrKCgg\nNjaWtm3b0qJFCwwGw4V1VBAEQRAEQRAEQRAE4S8ght4LfwlPscKOeU7WTC0KJpMnrZ9RTKs77fR4\nNoKf/q/gvONSlrhoPzaM6Nrgd2tk7fFy/ZtR6BQV5wkf9jg9ZqcH+bN9qIfOVI4S0DDtddC3S12s\nNhtpWbBtP7SqB0t+hefnBKtKIVjBOfBaePkeSM0Aox7iLsPC7InRcGfvYHibFAWfLYX83TBxVtnD\n8YudwaBUd4EfdwTcbgxWGwUH9lfY7vD33+F3uzCFheEpykHTmen62lrcbjeKotCsWTPS09MrvZ7B\nYKBq1arodDr0ej1ms6gmFQRBEARBEARBEAThyiWCUuGS0jQNT5GKqgJqcCEmnV4ifZuPNVOKyjxm\n85wSuv9fBFVamTi+ufTw7mAlKVgjdKihGiabGVXRkD5Pw7a1EK3YD24FtYzz2gtVpkwehKQ38vTM\nYLXmwjXwwLTS7ZSTVZspR+Hbl8Fb/jSof7rI0OB/j5yApVvA6a64vSwFg9wLoWkqmlrWkzmnnaKA\nFhw+rxps7D+4lwMHDpyej9RoNNKtWze2b99e4Xlq166NpmmYzWZRTSoIgiAIgiAIgiAIwhVPDL0X\n/nSapuEqVCjOCJC61sOeRS6ObfTid6sUpwcoPhEgvIqOG9+NpvENNvTm88fZb5lbQrObbedtr3r1\nmapEWSdhDtNhjdQjR5rQsjzgLqP08iR/jBVFM1HslDiSERzGPul/5d/H7iOwegelKl4vF48PBnWs\nuE2tpGD1a3T4hZ1TZzSiKQrWuLgK2yW2bYek1+F2u9m2bRt79+4ttWiTz+cjMzOTGjVqlHuOuLg4\nLBYLoaGhIiQVBEEQBEEQBEEQBOFvQQSlwp/KVRDAmaOQscPH3BuzmD8mlxWvFvLdY3l8dHMW2fv8\neBwqO+Y5WPFaAeYwmVtmx1CzU+lh2flHAoTElJ571BwmE9+49Krupxiuiyt3YScAdBLORlGYjbB8\nCzSuCYfSodBRwTHA7MXgO39h90su3A5PDgNz2bcLwMujIToUDBdYUWqNjQNNo/mosRW2az9pMorJ\njKIoHDlypMw227dvp3r16tSvXx+d7szvSZIkatasyTXXXIPVai21TxAEQRAEQRAEQRAE4UomglLh\nT+MuDKCpUHg8wML7c/EUlx7m7S3RWPRoHiUZCiFxenq/GEX6Ni9fjsyhxdAQqlxlKn3Cs4JPg0Vi\n8MwYrJHlvGRDdBhvqVpu3wK31uDrTTpUNVitmV1QeUgKwTZ/RdYXaoPUTPjxP8G5S88WYoEZj0K7\nxhAReuHnlCSJmKbNqd6jF/VuuqXMNtc+/xLRTZqh1+tJTU0t91yqqrJq1SpiYmLo1asXHTp04Npr\nr6V///60aNECi8WCJFWUXAuCIAiCIAiCIAiCIFxZxBylwp/CkRsg4NHISfGz9RMHWnlTYWqwYWYx\njfrb+PreXAb+N5ofns5nyVP59Hk5iq9GB5eZj6lnwF2oElPPQJ3uFpoMtGGN1iHryg7fZJsBY/8k\ndFWteD9OQz0RnNxTrmrFcHsNFqSH8fo8PV3aQfvG8Oqn8Pzdld9Xk5pgM1Xe7s9mM0ObhrDtAHz+\nLHi8cCAdYsOD1bARIRATcfHntURFgSRx7XMv0eLeB9g99wOcmRlENmhI07tGY4mOodDtJhTwer0V\nnktVVdLT01EUBaPRSGRkJGazGVkWn78IgiAIgiAIgiAIgvD3I4JS4Q9zFQRQ/SAhEVXbgMEiIcmU\nG5Zm7PDR6dFw3AUqK14t5OoRdn56tgB3gUJkTT0FaQGuvT+M+CYGbpoVgzlURtZXXp0o2w3IHWLR\nNQkH38mLG2XyJSNjngafH0rcEBYCtRKDCyU1rwPbD5R/zkeHQOwlXPFeUVS83gAmkx7dOcvXR9jh\nuhbB6lePD+omg8kIkXbQ/4F/uZbISCyRkYQkJhHdsDGapmG02ZBPntSZm0tISAhRUVGVnis2NpZq\n1aqJIfaCIAiCIAiCIAiCIPztiaBU+N1UVcWRqbLl4xJ2fe3E59AITdDR9OYQrhpuZ9GjeXiKyk5L\nNUXDHq+jVmczVduYuWNBHKYQmRtnxOBzquiMEooPDFbtgkLSs8nhpSf2NLngxk7w2VJ46SN47V74\nYAI8NRM+mgjXPQi5Reef59m7ICnmoi59wQoK3eQWuHj3o80cTMunZtUIxt5+NTGRViLCLafbSRLE\nXaKgVm8yoTedXy4bEhLCkSNHqFmzJgaDAb/fX+bxOp2OhIQEEZIKgiAIgiAIgiAIgvCPIMbICr9b\n8QmVj2/NYstcBz5HcGn44gyFX6YVse6dIvq9UXZFYmiiDkkn0eflSDJ2+fju8VxkvYSnSKUkI0Dx\niQArXy9k3qgcTmz34y4qfyX7C2G3wv+NCFZjLl4PM78FswmmPgDHcmDtjGAoWjMRYiOgbztY+w7c\nNxBiLnBF+fL4A1DiDFaznpJf6ObVGb9Qt+NbvDlrPd/+tI+p72+gwXXTmfq/9eQXuHC7fWTnOsgr\ncJKT56CgyEUg8Meew4UKDQ0lNTUVVVXp0KFDmUPpZVmmY8eOIiQVBEEQBEEQBEEQBOEfQ1SUCr+L\nO8/P8pcLceWVXTGavtVH7j4/VduYOPpr6bkuW91hBzTmjcqhTjcLPZ6NZNUbhRxZ40FTwR6v4+o7\n7XR40MTCB3Pp+FA4tbta0F1kZenZkmNh+VS4+f9g2jxYsTW4qnz7JsH9Y66HkX2DFZwWE4SHnDnW\n4QJVCy6iVN70mwEFZOnM/oISyCmEtxfAweNQJRbGDQouzLRx+3Hmf7+XEJsRh9NHTJSNp+5rz6De\ndQmx6lFUlfxCB9nZ2TiKc5AkifiEZCQiAT3hYZayO/EnMZlMtGrVitWrV9OpUyf69evH3r17OXHi\nBJqmkZCQQMOGDTGZTJjN5kvaF0EQBEEQBEEQBEEQhMtFBKXCRVMLfPjzFI6s8VTYbud8B21Gh5YK\nShv0tVK7i4VPhmUR18hI4xtsfDIkC+WsisuSTIXlrxRSs5OZm96LYcnT+SS1MBIS+/tfrmYjXF0f\nNr4XXE3+YHqwWlSvg6hQMBqC4abTDQXFwUrQgAK/7IS5PwS/79cebugYPM6gD7bJLYRlW+DHjWC1\nBMPWKjHw4Y/w9MzgtfU6uKsPaBoUlmjYwmvz+ovjiI/04/cW07RuOPklGqnZEtmFOqrGgt3oIyvz\nKLk52QCkpqYSEhJC5+u64HDqCLEZK7jbP0aWZWJiYmjfvj2bN2/GbrfTqFEjGjZsGLwfvR6L5dKG\ntYIgCIIgCIIgCIIgCJebCEqFi6IW+XC/vAf/kDrlr2x/UuGxAFE19VRtYyIkTkfDfjYc2QGyU3w4\ns1W6TrCz7IXCUiHp2Q6v8tBogJ8uEyIoyVL+UFAKoNNBfFTwq22j4Da/qwR3UYB9hXaeeE9HeIjE\nkK7Qog5s2BOc03TnoWDbnzYF5zVdNhUa14Cdh6HHI1DiOnON9xcFz/3GfRAVBsVO+PK54LXX/QbP\nvC/h9cMDgyXGDjChqDGs2K4x9j8S2QVnzpMcG8mcJzuSaNrKieOHAXA4HKxetZLOnbsAly4ohWAY\nGhERQbt27VAUBU3T0Ol0IiAVBEEQBEEQBEEQBOEfSwSlwkXRcn0oe4vRGYPD4CUd1OxoIvka0Bmg\n4LDE3m99uAtUbNE6rIqfPrdApmTm+wl5hMTqaHKjDXuCjDFEpvBYoMLr7frKSas7QzDZf/+w+/K4\nc05w5JtZFDW9jyfm6pn6ALg8oCgw+/vgMPpZjweH3d80CY5ng8MNvR6Drf+Dbg8HK1DPtWE3vDQX\nJtwGbm9w6H1UGNz3JtRKgu9eDQaoH/4AVzeAwc9IaFrpcxzLht5PmFn/dksK8jJwu4MXKi4uxuV2\nERoacv6FLwFTGYs9CYIgCIIgCIIgCIIAq1evti5cuDD8k08+ic7OzjbodDri4uJ8Z7dRFEUqLCzU\ne71eadCgQXnz589PPbXv4MGDhokTJyZu2LDBbrVaVVVVqVevnrtPnz5Fu3fvtvTr16+oZ8+ejrKu\nnZGRoZ8yZUrMjz/+GJabm2uIjIwMmM1mtVatWp4RI0bktWzZ0t21a9c6O3fuTKnsPlq3bl1v4cKF\nhxITEysOaf4FRFAqXBTfyiwA5MPFNBpkoeEgma0rdzBv9mZ8Hj+1mlWj+6udydhkwmIyYsx1oqzO\nIj8xmdpdLDQeYENVoNvECEITdbQeaWfbpw78bq3M6xVnBnDlq4RVCVZQaoqG5gyAooJRRjLpkPQX\nvyaZJz+LdQ/1If7ON3l5QTRv3BsMSCe8F6z8POXZ2dCqPvzwBvR+DDLyYMC18NGPZYekp3y/AZ66\nPRi2ZuTBo9PBZgme5743g9Wp7zwC//c/zgtJTz9rP0z+yMjjAxtyIGXL6e2ZGRnEx8Ve9D0LgiAI\ngiAIgiAIwj+BpmlkZmbacnJybH6/X2cwGJSYmBhnfHy8U5L+/EKr8nTs2NHVsWNHV926dT1jx46t\nERUV5U9PT991bjuv1yvdc889VUpKSk6viLx161Zz165d63Xp0qVo165de0JDQ9VAIMCnn34aPn78\n+KrZ2dmGfv36FZV13Q8//DB83Lhx1Rs2bOh68803j3Xt2tV5at+6dessjzzySPKmTZvs8fHxvrKO\nP9uKFSusmzZtCvnvf/8b/corr2T+3mfxTyGCUuHi+ILj7Q35bpoPD+G5W6ZSnHvmw42stFzWfbuF\n4c8MpkWXJhhUK/76YSTUt+BZ42He6Bz8rmAyKOuDc5YOeieahQ/m4i0+PzEMidXhyleQZFALffhX\nZBFYlweArm0khg5ReOUSNElFtYdiN1ZcaalpGt78bHxFeTR/Yga5tmbcpEJuETw3BzaX8TnL5hS4\n9VlY+VawEtTthdhw6NQcnng3WEFalj2p4FegUY3gvKhvjgsO5f9pU3B/oxrw654Ku8uidTIvjUwC\nzgSlsu7ig2FBEARBEARBEARB+LtTFEVKSUmJSklJSfD5fHpVVSVN0yRJkjRZljWj0RioX79+Rv36\n9fN0Ol05ZUl/vsTExHImFQwymUzatGnT0u+9994qp7aNHj26ml6v1z7//PNUg8EABKfBGz58eGGL\nFi3cbdu2bVjWuSZPnhw7adKk5C5duhR+//33h00mU6n7bN++vXvNmjX7e/bsWXvfvn2Vzp83ZcqU\nOIAPP/ww5oUXXsjU6//dUaFIXISLom8VCYC3VQjTxn1YKiQ929zJX+EsLkbN9yF1TiDlexcb/1dy\nOiQFUAOw+xsX694pptvTEWWep8kNNnIP+dFUcL20G+/sIygpxSgpxfjmpOIctx3tmIfDSz8hkJGG\noyCTkkDZffIV5XF0ycf8cn93Vo5sy8aJt5Cf76FmYrDys6yQ9JRdh+G3wzDwKWg/FurdBlO/hM/+\nD9K+gj0fwWNDSh+j0wXnL/X5QZKgZ2v4+Kcz+5VK5niFYJXruRWnSYlVym4sCIIgCIIgCIIgCP9Q\nPp9P/vHHH+vu3Lkz2e12GxVFkTVNkwA0TZMURZHdbrdx586dyT/++GNdn8932TIvWa78UmFhYerQ\noUPzAXJzc3VbtmwJiYiICJwKSc/WpEkT75AhQ3LP3b5ixQrr888/X8VqtaqzZ88+em5IeorBYODj\njz9OtdlsSkV9SktLM/z888/hRqNRy8zMNH788cfhld7IP5wISoWLoqttR0o0U6L5OLr3RIVtf/py\nPQGbhF+BnV85y213bJMXU4hMSJwOSQe1upro8Cr0+1zGflUJTcaquMz5aAnG81+xbgX19RNUadCT\nxQ+2puToXtTiYnxq6Q9zfMX57Jn1HDtevw9XRioAAVcJcZESLk9w1frKfL8ermkc/F5RYMGa4Hyl\n/kAwEL21Gxz4DKLDgsFrk5oQFwEWEyRGw9HsYNvTXfdCTCVvQXWTwes58+xiYmKQ5H/3pzuCIAiC\nIAiCIAjCv4uiKNLSpUvrFBYW2hRFqTDLUhRFLiwstC1durSOoiiXbxz+Bejbt68DQFGC+eWBAwcs\ns2bNKrNyrHfv3ucNu7///vurBQIBqX///vnVqlWrsIo1MTExMHTo0LyK2rz55psxXbt2LerVq1cB\nwDvvvBN3gbfyjyWCUuGiSGEGrM82Jv1AVqVtD+0+jj9EJnWtB62S6sl9P7pocqON67/QkdZzAQ/s\nvI1B8/rz8Lfj2ZG7lTVpq5nTbDXu2XUxP1QXKfbMIkNaSQB9hoGw5PqsfXMEmqMIp1I6mPXknODo\n4jmltmmqgpq9n9CQC6vuDCjBKtFS/T4K362D8e/AXa9AagasmQ5Du8GKrZAcBz9thP7XAOd8zvPh\nEhh1fcXXfHyIl6zjwelNYmNjqVO/BU73BXRWEARBEARBEARBEP4hUlJSooqKiqyqql5Q8KmqqlRU\nVGRNSUmJutR9uxBz5swJ/+677+ynfo6Li1MaNGjgAhgzZkzNe++9N8nlcpW6t8GDBxefvZDT2rVr\nLbt377YC9OrVq8y5S8/1wAMP5JS3z+12Sx999FHMuHHjsu+7775sgE2bNoVs2rTJfHF3988iglLh\nokiyhBxnwRplrbStxWZCUsFTrCLrodZ1ZloPt9FssJWQuNKJo84okXxzCc/9MpEiTy596vUgObIK\ny1NWcvvsu9lxfBcWo4U+7w4mp7YPy+MN0NW1nzl+v0RYcgMc2Wk4c46ilJSc3qf4PBz+ekaZfcz4\nZCJVojV6tq783nu0hu0Hzt/+2dLgAk87DkLfJ6DAAVPuh9t6gMUIXa+CYd0hNgLMxjPHfb0aOjaD\nXm3Kvt6dvTU6NfMTEZVEnz798ElVePej7VjMoqJUEARBEARBEARB+HfQNI2UlJSEyipJz6UoipyS\nkpKglbeC8mVSUFAgf/DBB9Hnbp8xY0baqZXuZ8yYEV+nTp3G7733XqSqll0c9fPPP4ee+v6qq66q\nYHnpMyIjI8uttJo5c2ZkbGysv1evXo4ePXo469Sp44Yzc5b+W4mgVLhokiyRXD8Bk8VYfhtJYuB9\n3TChUPc6mbs/iaRbKxet/Bm0D81l6AtWbnwjHFNo8AOThreDVBLgP1WfZPy+Gxi/7wbm1n+NX8cs\npUWVZsxc8wFJ4UkE1ACTV75O8Y4MzPfUAlPwJayZNBS/F4Ci4/vQnfVGqPi8uDKPltnPgt0b0YrS\naVwDqseXf8+J0dCyLuxOLeMcJRAXCff0D1aIfrkcnB6wmqFtI2jTEJJjIcQCYweeOS6gwOBnYHhP\nmPc89GsfvMbgzrBsKnS9SuL7jVasoVVodf0cHntxOWPvuJroSFv5HRUEQRAEQRAEQRCEf5DMzEyb\nz+f7XRVDPp9Pn5mZedn+iC4oKNA3a9as/qmvevXqNYyLi2u+evXqsHPbdurUyfXTTz+l1KxZ0wNw\n4sQJ45gxY2o0bdq0wc8//3xenw8dOnR6aG18fHzg3P0Xa8aMGXGjRo3KPvXzyJEjcwAWLlwYmZub\nqyv/yH82EZQKv4st1MKND/Usc1/fOzvy1pInqeW1o25xEprvw6r60aWVENiQh7IiC17aSczqI9w6\nPYLq7U3EGY2EP5uJ9a0sAutyCazLxTI9i7jJ+Xzc5x2aJjVm7oZPGNr6Zhb+thh3CxO+5dkYOsUC\noF1tJGvXKgAs4XFw1jyeOqMZa3y1cu8lf/EUft6s8dEzkBRz/v64yOCiTWt3npmj9GxNakKj6sF5\nSrPyoXHN4Pb9xyA9B4wGqBILVWLg0SEwvNeZYx1uGPo8vPIJvDIG5k/WeHWMhknvp25iMccOrOGG\nkXO57YamLPrgVkymf+17lSAIgiAIgiAIgvAvlJOTY7vQIffn0jRNysnJuWxBaURERGDHjh0pp772\n7du3Z/fu3b9Vq1bNW1b7a665xr179+49EyZMSLfZbCrA7t27rT179qx/zz33VDm7ujQQCJx+Bna7\n/Q/Nybd48eKQjIwM4+jRo/NPbRs9enSe1WpV3W63PH369PMqYP8tRFAq/C5Gi5HOQ9ow/P8GYgu1\nnN4+dvLN9G/UDP1TKXin7Mf3YSrul/bgenEPhi5xGLqfKdtUdxViXpXOwBdC8UzajZbvO+86WqEf\n2ysneKv3q+zN2Ee1yGT8ip//Z+8+o6uo9j6Of+f0k3bSG6RAKKH33ruCqBQBRVGxi1hQsSsWbAiI\nBRUFRUVBQAQBadJBpHdCICEEkhBIz+ltnhe5RLkQUK+PIv4/a2UBM3v23jNzyIvf2cXhd+JZm4+u\nXSSahkEUF+7HWXoGfUAw4bWbowkKqqxDazBSc9D9Vd6Lz+1hwx6FBybDzGfg47FwQ7eKkZ0fPFoR\nkj76Pmw7XDF9/r/d2b9iyv1Xq2DeWrjzDWh8W8WI0c37KsJSAJMR4iJg0gMVmz69fi88MRy+exXm\nv6ISFuRn2Zr9vDxxISalAKPWwYgB9Zg/bQiD+tZn5rw9GPUy7V4IIYQQQgghxL+Hx+PRnt3d/vfy\n+/2K1+v9W0cc1a5d2/3rkZv/zWQyqa+++uqpw4cP77vpppvOaLVaVFVl2rRpMY8//njc2XIRERGV\no0jz8/P/p3uaMmVKzJAhQwp+HbiGhYX5r7vuukKAGTNmRJ3dcOrfRlIX8YcFhQbQ8+b2tOnbhKLc\nEvQ6LbHFepxvHT6/sM2L4600Al5phHdH0S+haGYZao4D9cwFv1wBQC10E1EQQdOERtjdFctwGLQG\nsPtQLHq8ww1sePF2AJqNGI/OFIRZF3ROHabIOJKvu5OshZ+cV7/XWkzNJD9frtDQ8+GK6e9tG1Sc\nm7EEtqVV/H1AJzhw7NxrxwyFQ1mQW3Du8TIbXPUY62Q1KAAAIABJREFUbP8YVu+APq19rPspjcMZ\nBdRMDKNbu2RGDzBjMun/c4XCydwynn7tBwqL7cyYs5vEahaiIwMpKnFwIreMrd/fRUTYpdeGFUII\nIYQQQgghrhR6vd6nKIr6R8JSjUaj6nS6vz3xq1evnvNSZapVq+adNWtW9n333VcwdOjQlNzcXMPU\nqVNjn3322fywsDB/69atbe+//z4Ahw8fNiYmJv6h6ffp6emGNWvWWFJSUpxNmjRJ/fU5h8OhAThx\n4oRx7ty5lmHDhv2mTaOuJDKiVPxPdAYdliAzSbFRVIuJxPXlcQCUED2aOBME/OpLDp+Ke1EO+t6/\njCrVJATi3VZ4yXaC9vq4vd0tLNm3nJZJzTBmeNFUM+Pw5bN0XEcMgRY6Pf4FSR0HorNEoFHO/Wgb\nQsJIHfkszZ76iMBqKZXHI5p0oPbwMdzc55fftzvTYeqCip+zIalOC/07VASi0WHQpxWsnlKxrulj\nUy/c53I7fLcBGqaAzanhnicX89yENQwf/S01OrzDrO/2kZtfRlm5k6JiO4EBenb+cDdd2yUDkJ1T\nyvY9uQQHGtm0YCR1Uy6LzfqEEEIIIYQQQoi/TFRUlE2j0fyhHZkURVGjoqJsf3affq+BAweWXXPN\nNeW/PjZy5MiEC5Xt2LGjffHixUe0Wi1Op1Oze/duE8CAAQPKgoODfQDff//9eWue/lYTJ06M7t27\nd0laWtrBXy8TsGfPnrT09PSDrVu3Lgd4//33o/9oG/9kMqJU/M/Uch8ajYK/0IW2mhnDfbVQvSpq\niQdNtBHV6sU17wT+DCvebUUE9I2ncpK9X0XR/YYvhXQKjeIbsDlzC8tum0fg+2Xob6iOO9RFv8lb\n0BoD0FsiMRqrHnFpCAmneq9hRLXsjt/rQdFo0AWEoA8IwlEIY4bApG8ufO2rd8OZEnjuVmhWG8xG\nWLkdHpxy8W6v2g5dmoLbA13aJPHd8jSCAg18+No1NKkfy2ff7KbM6qJjqyQa1InCaNDy7cdDsNm9\nFBTZsQQbCQw0EB0hGzgJIYQQQgghhPj3iY2NtRkMBq/D4ah6R+kqGI1GT2xs7N8elP7ap59+Gnb7\n7bcXr127NsTn86HVnj+LvlmzZs7U1FT7gQMHAiIjI31QsS7pgw8+mDd+/PjqX331VdTzzz+ff/Zc\nVWbMmBGWmprqbN++vQOgrKxMM3v27MilS5deYCpwhccff/zUDTfcELxp06aQvXv3Ghs3blz1FOAr\nkIwoFf8Tv9WDWubBl16OEqBF1yUax9vpOF4+gPPddOzP7cM1KwvTbTXQtQwHnwrKL8GoL8uGruOl\nv6Qwdo3h5yPbWHrrNyQu1aCJN6NvGUFwbDKWhFSCohMvGpKepSgKpvAY7MbqZFnjee2bIJ74EPZn\nwn0DKtYkrVXtl/L1k2HOixARAkVlUC8JCsvB56/YiOlSdNqKkaUGA7RoHE9ggIEVs25h3tKDNOo5\nlWfeXM0bUzfR//av6DToU7JOllJa5sLv95MQH0KNxDAJSYUQQgghhBBC/GspikJqamqeVqv9XRsY\nabVaf926dU8pyh9a3vR3ObvpkqpefODrF198EZqenm4EyMnJMU6cOPECW0qD1WpVTp48aWzQoIG9\nUaNGlUHlCy+8kN+tW7fSwsJC3fDhw5OdTmeVN7dkyZKgzMxMw9mQFOCtt96Kqlevnr1Dhw5VJhqD\nBw8uS0pKcqmqymuvvRZbVbkrlQSl4n/jUVECtfjSy1FLPDgnH0YtPndTJv9JB/bxBzAMTUTbIhxX\niIoSVvFFkL5DFEq4Hk3toAvVDoAmJQhthInmjlqk/hBIUKfqmB6ojSbkd3+ZBMDpYti8HzbshbTj\nMGVuxXqinR+A1vXgnYcrptWveQeeuhniI+HxD6C4HIaNgy4PVISmbepfuq0h3UGvA71WoaDYxZOj\nOjJ15la+W5Z2XtmcU2VcO/JrikudrNyYScbxYgqLLqsvvoQQQgghhBBCiL9campqocVisf/WKfga\njUa1WCz21NTUS6/19yfIzc3VA1itVq3Vaj0vvPT7/Xz22Weh9913X40777yzsk9PP/104qhRo6rl\n5ORUzvjOycnRXX/99TW1Wq36+eefn7NTil6vZ/HixRnXX3994YoVK0LbtGlTd/78+SFng1qAU6dO\naZ999tmYLVu2BL7yyiv5Z4/v2rXLNHny5Ljk5ORLjhBNTEx0AcydOzdy/vz5Ib/zcfyjydR78b/x\n+8GgRds0FOeMTKjqV5bTj2dpLr7hsTy49klef+4ZLBs96LtGceroRiz31kQzXYP/YNk5l2lTQzCN\nrYvDfobwWtFoGxnPC0g91lJ8roovQ3SBIehMFx5ZanNATgFM+BrW764IMK9pD+vfg6c/gjW7oNcY\n+P516DgKzn4RNO1xaFkHXB4INFcc25YGlkC4vlPFOqQXEh8JjVMgOADKbD7SjuQzfmx3Xpi4psrH\nWVhsZ9nao/TrUYdB98zhh89vJiJcRpQKIYQQQgghhPj30mq1as+ePY+sWrWqdmlpaYDP56ty4J9W\nq/VbLBZ7z549j2i12j+0tulvtXXrVvPSpUtDpk6dGgMVmyGlpKQ0ioqK8pwt4/F4lNOnT+tLSkp0\n7dq1K0tJSfEAdO3atfSOO+44s379+uD+/fvXcrvditPp1Hg8HqVLly5l06dPP5iUlOT57zYDAgLU\nBQsWZC1btqxgxowZEWPGjEm86667tAkJCa6IiAhv7dq1nffee29Bs2bNKjeQGjt2bNykSZPifD6f\nMmfOnMjNmzcHT58+/VivXr3OGZ21c+dOU//+/Wvn5uYaAHw+H4MHD65dr149+8GDBw/9fz3Hy4ly\nqWHBV4qWLVuq27dv/7u7ccXxFbrw59jRhBiwjd5x8cIGDZ63a1Nnckv61O/J+9e9hba0hG9H18Ic\nFku7kR8QVa01SpoHVAVtkxDKrcfw6B2Y4+oQZAlHo9HgLi3C67Th97hQtDpOb/2RI19OwOdyENf5\nWmrfNAZTZDxag7GyabujYk3RG14A33+t4GEJgvmvwFMfVgSg7z4MsWHwzdqKKfePDKkIWCfOrhhF\n+vx0qJNQEaBCRfC65Kdz66xVDRa+BvnFFdP1s06WcPvDs3jivg7c+sh3F31Mndok8dxDnXlx8jru\nH9GKXp1qEiXT74UQQgghhBBCXIYURdmhqmrL31J2z549WU2aNCn4o235fD4lLS0tIi0tLc7tdutU\nVVX8fr+i0WhURVFUo9HoqVu37qnU1NTC/++QVPxz7dmzJ7JJkybJFzonI0pFlVS/irPYhsfuwu/2\nYQgxow80og/4ZUSnolNQSzwQ+humwbv9FNqKAFhx6EfKBzgIC9KCouAoPsXqiQMwBIURXrMJAMWz\n96LRGej5/CL2PDuUFs9/hsdayr4pj1K0ryKZ1BrNxHcfRMtxn7Pj5ZFkL5nJyRWzaT9pMaH1WuBy\nenHZ3ZT7Ahnygva8kBSg1Aq3vwpTx0D/J2HeWpj5DFzToWKNUY0GAkxQLxm6t4APF0L6CXh9VsXm\nTnf0g8dvgtU7wOOt2LwppRq43FA3EY6ecPD6lOUoioLff+nf0z6fn9AQE99+fR0Omw+tQX63CyGE\nEEIIIYQQWq1WbdCgQUH9+vULTp06FXjmzJlAr9er1el0vqioKFtsbKztr1iTVFy5JCgVF+Qud3Jq\nexbb316BLa8UAI1eS3LvBjQf1QNz5H/WFDVo0NYMQrV7wawFR9UbrmlqBJJRVLG8hqqq7Dx0kB51\n2tL+wWlsnnJXRbvWYk7tXQtUhKA9n5yDp6yERo9Mxu/1UJaxH5/TXlmnz+XgxA9fUnJoO82fnsam\nh69Gozfg8AdQfspP2jGVYquR5OoaPn+mItzcm3F+306crphanxhTMeV+bwb0a/fL+ZgwuPWqilD1\n+9fh/kmw7GfYlwl39YdrO1QEpnodOD1QboOoUBWv10fG0Qy+X3EIk0lHg7qX3rjqqq61KCqxoz/l\nxW924TbbcbsdxBouuMazEEIIIYQQQgjxr6IoCnFxcba4uDjZ2EP8qSQoFefxeXyc3HiETS+cO0Xc\n7/GRuWQvxUdO0+OdGzGHB6HoNPhyHfhyHRh6xeBelFtlvdZrg5m8dVzlv3WKjiVPOOk/YTDXvteS\nfXPf4NSeNaBAYvuBNBz0GDpzCD63DdXnA42WgOo1qHf3S3jKi9nx0m2VdZVnpVG07yfiuw0mYeRk\nlu0JYsxTGkqsv7RfLwneewRenglrd53fv70ZUDMeujaDIyfA36ZiNCmAVgvJsRWB6q4j8PaDFaHo\noSyICq2Yoq/V+LG7FPw+Py57EcfLvIyb9CNvPdeHvSvv47O5u8nOKaVv99osXX3kgs8owKynd+cU\nOg6cgarCvbc35ba7UykNKsevqsQbLx20CiGEEEIIIYQQQojfT4JScR5XqZ1tk5ZXeb44/RQF+3JI\n6FIX1enFvfIUxsEJUC8E31Ervv/akAnA1yOMA5YT7MjeDYBeq6dOVF02F/twe4MgpBENb5xJ/UFe\njIEqhiAvPreVQ4umcHjph7hKC9Do9CR1GEzTW8bhtpXS4vnPzglLvS4fqfdO4of9wYx84/yh9oeO\nw8BnYdlb0P0hcPzXPm8hgaAo0Ksl7Er/JSQ9K8BUMZU+NhxsTkCFhFYQGXq2hIaiEjt7D+UzbtI6\nDh09w7wPh7BzXx4BZh33DG/B3CUHeevZ3hQW2/l5V8459QcHGZn9/mBeeWc9Xm/FjnXvf7KTkBAj\nd9/ZmFmnv2NU/C0EaM1VvhshhBBCCCGEEEII8cdIUCrOY80twV3quGiZQ7N/JrJBDPacw5gCgnC8\ncgDT6NqYRtfBf9yGe1kearEb4kyUXxXANsc+7vlmTOX1I1oNJzg0hEavBvHYp3CmVKFBdYWbu2g5\nvcpGdDUNBvN29n79SuU1fq+HY+u+5uS2JfSduAmDMYiQ2k0oO7KHaj1uJLjW1RT5Qxj7QdXrkZRa\n4csVMKwHfLr0l+NaLXRoBI1qwisz4YNHq753S1DFz4WEhwbQoWUiX0wZQJnVRbnVRbXYYJKrW9Dp\nNAzuW5/vVx7m9ad6otdp+WzubmwOD22aVqNVk2q8NGUdy9cePafOdz/awfAh9WkSWI8yn1WCUiGE\nEEIIIYQQQoj/B5pLFxH/Fqqq4vf7UdVLbx7kLnNSlnWYbeNuhM4m1BIPjpcPYn9+H74sG4bBCfie\nqsHsNlvps+RGbv/2Adw+N4qicGOzoTzY+SEefDeI1vcpfLZMYclP8OZcDU1Ga9kXEsTpPCjNa0mN\nLred17bHXsaWqaNwlhZQ747nQKMhecDDuC2xWB0K2fkX7/v8tXBVm3OPjRoAQWZ4Yxbc1lfFpHeR\nkVVEZnYx+Wesv+mZnKXXa0mIt5CaEklqrSiaNYwl1GLGanOz73A+XdomkVQ9lJpJobz2VE9qJYUx\n/4dDdBgw/byQFMBqc5ObZyXWEIXL7/7N/RBCCCGEEEIIIYQQv52MKBWUFRdQkHeCtQs+x2m30aht\nd7pOG0za9O2c+jnrgtdYUqI4s+0H3CUFlBUeJKR3HXwr8lHPuHDPzgZAkxJIv1u60mhoQzZn7MCs\nBNKlZhc8+WY+XBDOt+vPH/mpqvDINA1rXgtg/wQrvV94nGPrPjuvXP6+9eiDQzEEWohs1gXraS/a\nepG4yi99vw43GPUVfw80w+hB0LMFjJ8Jkx5QsZWfoVHPz8k/U7HAaa3kcF5/qifdO9QgzPLbR3Nq\ntRpCQ0yV/w6zmKlX+9w1RvccPMWXC/ZxLLv4onX5fODFg0Gj/83tCyGEEEIIIYQQQojfToLSf7mS\ngnzefuwW0ndvqTy2cclszEEhPPrGbBSdhrxNmZXnNAYtyd3q0WxkZwq2lxHVohvlJSUEXZ2EtkYI\n/kUnUPOcFWXNOoK84bA6jOo7k/G6VJYf9ND383DeW3TxwcxvLtDwWL8gbAVuAiKrYS/IOa+M12VD\nrw+g+r0f8d2OSN6dpvDFs2DQg9tTdd2tUlVqxPjY8J6GpFgw6Xw4PXomj/YyYeqPvPXRT+eUP5pV\nxOB7vuGdl65m5NBmBAYYfsuj/U1MRh3dO9Rg+kWCUp1OQ92EaNaVr6F2VI0/rW0hhBBCCCGEEEII\n8QuZev8vZisrYcb4R84JSc9yWMuY8NgNpI5sgaKt+JjU7N2Q66bdRbNajVHfyyZiWxOadZ9KjS43\n4DdrybdYsN1WH99LzTF91JqsJkms+sLDyb1evE4Vj0Pl2ul6HFov5faL923dXoXQuka8LiMp3W7G\nkpB6Xhl9YAjluur0Hx/LI1N1ZObCt+thaPeL1/3MCEgIzCVg36scfKcfBz4ZiaF4KxpXPtO+2lnl\ndY+PX0lpufPilf9OoRYjo29vjUZT9bqqw65rgC7AT/ew9gTrqlgcVQghhBBCCCGEuPypv2dpOyH+\nbJf6/MmI0n8xu7WMHWuXVHne5bCxffNial7TCDzQrE8bPM+k4ff+8qHyHSxDCc3G/EpjTu33EtfY\nwPav7KR09pLY0kpy0k70xXvwh9WmKKEjox96iMef/wgwVdkugF4Hql8lNEFLybEymtz0PMbgcLZ+\n9DClJ9KIbtABvzaEyfOM7P9lwCsffw+L34CDWbDj8Pn1PnerSp2oEhbckVwxz/8/Mld/SWzT3vz4\n2UQ6D1+Iw3n+kFSXy8u6n45z4/WNLtr33yMqPJByq5vZUwdx0wPfVu52f1aH1gm89nQPtCY30caY\nP61dIYQQQgghhBDir6YoSoHT6TSazWbZgEP8Ldxut15RlCqn9UpQ+i+Wlbbnkkn6rk3LeGTCLQQr\nIdju3Q7e88urJR7c4w/QdFwqZ/I8tL3DQLBnO8Y5A8FZAkBJ388Y99JLnD59Gpe9kLiISPIKq273\nunZ+bJlurNW3cXjJBxxe8gHBcTXpPHYWP713P+0e+IiiMjMfLz53UHSZDW54HmY8CaeLYdZKKCqD\nekkw4iqoVQ1Wj65xTkh61qndKwiIn8ZdQ3vwzsxdF+xXdm7pRZ/X76XRaEiuHorJqCNt7QN8s3g/\nm7efJDTExH23tiApwYIlTE+QLvRPbVcIIYQQQgghhPir+Xy+L8+cOfNwQkJCoaJUPbNSiP8vJSUl\nwX6/f3ZV52Xq/b+Yorn061c0ClqjHu9PheCpOlT15zmhyE2hcpSgOA/G+X0qQ1IMgZSFpHLo0CHq\n1auHzm3lhVu9VdZlNMCY61RSWpaxf95jlcfL8zJZP+EWuj+3gLSvMrCVei44hT+/CPqNhbfnQsfG\n8OlTEGGBCV+DrzAdt63qsDP7x+ncOTClyvMN6kZXee6P0um0VI+zkBAXwj3DW/LJm9cydXw/2jdP\nolpUKEG6wD+9TSGEEEIIIYQQ4q/m9/tnFhcX783KygovLS0N8nq9WpmKL/6/qaqKy+XS5+fnh+fn\n55f4fL5pVZWVEaX/YjVSm6JoNKh+f5Vl2vUZTIAxGNfu3EvWp8104o31YnOpaNqPx7BhbMWJkOrk\n5OUz5c2P8OeGcXiahuY3eXj5Vg0vf6U5Z+OlSAt885yPmKBitr0zhNLsQ+e0UZ57lNKTxzi1LZvw\nfn40Gqiq+3uOwuHsirD0sx9g81Q/hybfctF78Lrs6P12NBoFv//cX9aWEBMtGsVd8jn8UQaDjnCD\n/JcUQgghhBBCCHFlatGiRcmOHTtuKCoqGlZeXt4XaKyqasDf3S9x5VMUpdjv98/2+XzTWrRokVVV\nOUll/sXMgUF0uHoIG5dceMRxYHAorXtcg9VtRW+89OhTr85P3bp12b/3AClxI4mquxXt4XmgaKnf\nqAe5O8Dp8hOe5GbDC4U0vyGIXZOD2JKhUORUaFRDJbW6h4J177Ls5Wfxey68ZMmJLd8RntqRkp1H\n6du6BYu3VN23Id0g74yXbe87iLU42XMmGwCt3khC22sJjE7C67Ry4ufvsRfkAKBodefNzNdqNcx+\nfxDhFvMln4MQQgghhBBCCCEurEWLFm7g8//8CHFZkan3/2IBwRaGPzqeFl37nXcuNDKG5z9eiMdv\nZcSX91DayXDxyhRQGwSjqio+1cuJPQqO5s/jq3UNZf3Xse8bL1unl7Nnng2TRcPQGVG4T3lZMjwP\n3ewCrg4vJz7gECcXjGHPF2OrDEkr2lJAhZyFW3jlFieBVWSXYcHw7AgPdYqn4tzxEZ7SfPpO2MCA\nT9IZMD2d4Gp1KD1xCI/DSrvRH9F57CzCU5oRGRtD3x61gYqA9Lo+ddm9/F46tU7CaJTvFoQQQggh\nhBBCCCGuRMq/ZS2Ili1bqtu3b/+7u3FZKi8pwlpaxM8rv8Nlt1G/VUcSokMxnNrEE8cz+Wr7fFbe\nvoA6H4H/+AUWBQU0PaLI6ezHHBmM3e4gd2Ek9Xt6QIU593nxOM79nOmMCte8FcHeeVYy1ztJ7Wum\n5YMqflchGYsmcXzdV3idtgu21eOF1ZRlmIhunICt2El5TBKj3tWzaV/FeUWBHi1UJt9dinf/p0TX\nac7+eW+Rs30pqt9PQGR1UvuPIrHd9bitxfi9blAU9KYgFI0O25lsDCFRmCOr49GEoDPosQSb/tRn\nLoQQQgghhBBCXAkURdmhqmrLv7sfQvwZJCgV5zt9EN5tQN7IjbT44FZcXhfxoXEsGT6b8Bll+PeW\n/VJWq6DrHYP96hBW/LSarl27olcDmTfcytAZEXw9ohBHyYUXEdWbFYZ8GoXdW8LO/T+xetNy3G43\nTRrWp1/vbuT8+DGZP3x4zjWhyQ3p8fwP7PtkJ5lL96L6VEISI6g+tDPBjWvi1Rkx4sHjK+Hg/o20\nqxPLyqd7XHCEarVWfanV81bWvTYUoyWSun3vJaZBR9aMH4zXYcUUGk270R8R16Q7hsCQP/URCyGE\nEEIIIYQQVwIJSsWVROYRi3N57LBuPABuVcXldQGQW5JHn88HM67Xk3Qa0Qay7KDX4E80YgoOYMWP\nq9BqtYQEh3B8kxdzuJbCTF+VISmAx6FScMTNV1vfZuPGjZXH09PT+Xbh9zz3xBhq+H0cW/4xAGE1\nGtPt2QWUHnOgaBR0Jj0em5uy7EIOTlgAQEhyBF0nD+PWUffy5CP3s3ny7VVO48/ZtpR6146m35St\nHN8wl8NLPqAoczf9391BWc4RstbPYf3rw+jx4vfEN+v1pzxeIYQQQgghhBBCCHF5kqD0X85W6EP9\nT5ZpDFbQ+x1QnAmAQdGg1+rx+Cq2pT9dfob7Fz2KWW8iMTwBr99L4epilt49H7/fT5MmTfC7Nax4\nupwG1waSs+si64z+x4ltLkJCLOcd9/l8vPzGJL6YMY3gsGiqt+6P2RKH2+pFZ3RS+/pUUoe14OS6\nDHZ/uLbyurKsQk4dyCYmJoa4sCDSco9etP0D306k1Z1vYQyNoueLi9n60Rjc5cXYC06S2H4gDQc9\nzo7PnyEsuTHmsJjf+FSFEEIIIYQQQgghxD+NBKX/Uo4SH8d/crFzVjnFxz3U7qqnwdVeYuob0IYk\noLCF4COLua5JP+bt/O7caz1ODucfAeC+TndSlFdIyxYtqRabwOfXFqP6oflNgRxZ7bhkP3QBKm63\n64LnfD4fP6xcw4jhT1GedYIfH3qIzB++R/X50JlM1B16M20eewZTRD+2jF9SeV3eqnT6XnU1iqP4\nku3bTmdTlLkHrdaA1+Wg46OfcnjpNArSf0ZvCiK58xDa3DMFv9dzybqEEEIIIYQQQgghxD+XBKX/\nQtYzTnK3lRGf4iD56RyMQRowBqGkLYJlu1Bbj4IDcwkoSOPpPpNZm76BAmvhefUkhFXn/s53EWqw\nUHTMzxfXl1C9mYEO9wRhMR4htaOFTe9dvC+JPXy8+2rVa8fu278fa2kpc3q2xVVaCkB002a0enQ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jCX6q2vQaPTs++b18la/w0AWz96hOj6HTDWaXXRZyaEEEIIIYQQQgghLi8yovQf5nQxHM/1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\nH/fdSGmNG07hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "long = pd.DataFrame(embedding, columns=['pc1', 'pc2'], index=mut_sample_mat.index)\n",
    "cancer_type = [i.split('-')[1] for i in mut_sample_mat.index]\n",
    "long['cancertype'] = cancer_type\n",
    "fig = plt.figure(figsize=(20, 12))\n",
    "g = sns.scatterplot(x='pc1', y='pc2', data=long, hue='cancertype',\n",
    "               palette=sns.color_palette(\"dark\", n_colors=8)+sns.color_palette(\"bright\", n_colors=8), s=100)\n",
    "plt.xlabel('Component 1', fontsize=25)\n",
    "plt.ylabel('Component 2', fontsize=25)\n",
    "legend = plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0., prop={'size': 23.5}, handlelength=1,\n",
    "                    markerscale=2.5)\n",
    "plt.title('UMAP Embedding of Mutation Frequencies for Samples', fontsize=30)\n",
    "fig.savefig('../../data/pancancer/TCGA/mutation/UMAP_{}.png'.format('SNV_CNA' if USE_CNAS else 'SNV'), dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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